complete code

imageInput.c

@@ -49,7 +49,10 @@ GrayScaleImageSeries *readImages(const char *path)
     }
 
     unsigned short numberOfImages, width, height;
-    readFileHeader(file, &numberOfImages, &width, &height); // Hilfsfunktion von oben
+    if (!readFileHeader(file, &numberOfImages, &width, &height)) {
+        fclose(file);
+        return NULL;
+    }
 
     GrayScaleImageSeries *series = malloc(sizeof(GrayScaleImageSeries));    // Speicher in Serie-Struktur reservieren
     if (series == NULL) {   // kann nicht angelegt werden

matrix.c

@@ -99,6 +99,7 @@ Matrix multiply(const Matrix matrix1, const Matrix matrix2)
  }
  for (unsigned int i =0; i < resultRows; ++i){
     for (unsigned int j =0; j< resultCols; ++j){
+        // compute dot product of row i from matrix1 and column j from matrix2 
         MatrixType sum = 0.0;
         for (unsigned int k =0; k< matrix1.cols; ++k){
             MatrixType val1 =getMatrixAt(matrix1, i,k);
@@ -107,7 +108,7 @@ Matrix multiply(const Matrix matrix1, const Matrix matrix2)
         }
 
 
-        unsigned int resultIndex = i * resultCols + j;
+        unsigned int resultIndex = i * resultCols + j; // store the result 
         resultBuffer [resultIndex] = sum;
     }
  }

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

@@ -8,6 +8,36 @@
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
+    FILE *file = fopen(path, "wb");
+
+    if (file != NULL){
+        const char *fileTag = "__info2_neural_network_file_format__"; // write the file header
+        fwrite(fileTag, sizeof(char), strlen(fileTag), file);
+         for (unsigned int i =0; i< nn.numberOfLayers; i++){ //write each layer to the file
+             Layer layer = nn.layers[i];
+             int inputDimension = layer.weights.cols; // extract inputDimension from weights
+             int outputDimension = layer.weights.rows; // extract outputDimension from weights 
+
+             // For the first layer, write both inputDimension and outputDimension
+             // For subsequent layers, only write outputDimension (inputDimension = previous outputDimension)
+             if (i == 0) {
+                 fwrite(&inputDimension, sizeof(int), 1, file);
+             }
+             fwrite(&outputDimension, sizeof(int), 1, file);
+
+             // write weights matrix data
+             int weightsElementcount= layer.weights.rows *layer.weights.cols;
+             fwrite(layer.weights.buffer, sizeof(MatrixType), weightsElementcount, file);
+            
+             //write biases matrix data 
+             int biasesElementCount = layer.biases.rows *layer.biases.cols;
+             fwrite(layer.biases.buffer, sizeof (MatrixType), biasesElementCount, file);
+
+         }
+         int endMarker = 0; // write end marker (0) to signal no more layers 
+         fwrite(&endMarker, sizeof(int), 1, file);
+         fclose(file); // close the file 
+    }
     // TODO
 }