diff --git a/I2_NeuronalerAbsturz/Start_Mac/neuralNetwork.c b/I2_NeuronalerAbsturz/Start_Mac/neuralNetwork.c
index bd8f164..c94d969 100644
--- a/I2_NeuronalerAbsturz/Start_Mac/neuralNetwork.c
+++ b/I2_NeuronalerAbsturz/Start_Mac/neuralNetwork.c
@@ -67,14 +67,14 @@ static unsigned int readDimension(FILE *file)
 
     if(fread(&dimension, sizeof(int), 1, file) != 1)
         dimension = 0;
-    
+
     return dimension;
 }
 
 static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols)
 {
     Matrix matrix = createMatrix(rows, cols);
-    
+
     if(matrix.buffer != NULL)
     {
         if(fread(matrix.buffer, sizeof(MatrixType), rows*cols, file) != rows*cols)
@@ -126,13 +126,16 @@ NeuralNetwork loadModel(const char *path)
 
     if(file != NULL)
     {
-        if(checkFileHeader(file))
+        if(checkFileHeader(file)) // __info2_neural_network_file_format__
         {
-            unsigned int inputDimension = readDimension(file); 
-            unsigned int outputDimension = readDimension(file);
+            unsigned int inputDimension = readDimension(file); // ein int
+            unsigned int outputDimension = readDimension(file);// noch ein int
 
             while(inputDimension > 0 && outputDimension > 0)
             {
+                // ließt zwei Matritzen (je eine Zeile mit allen Werten hintereinander, durch Leerzeichen getrennt)
+                // 1. Matrix: weights
+                // 2. Matrix: biases
                 Layer layer = readLayer(file, inputDimension, outputDimension);
                 Layer *layerBuffer = NULL;
 
@@ -142,7 +145,7 @@ NeuralNetwork loadModel(const char *path)
                     clearModel(&model);
                     break;
                 }
-                
+
                 layerBuffer = (Layer *)realloc(model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
 
                 if(layerBuffer != NULL)
@@ -201,7 +204,7 @@ static Matrix forward(const NeuralNetwork model, Matrix inputBatch)
         {
             Matrix biasResult;
             Matrix weightResult;
-            
+
             weightResult = multiply(model.layers[i].weights, result);
             clearMatrix(&result);
             biasResult = add(model.layers[i].biases, weightResult);
@@ -248,9 +251,9 @@ unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
     Matrix outputBatch = forward(model, inputBatch);
 
     unsigned char *result = argmax(outputBatch);
-    
+
     clearMatrix(&outputBatch);
-    
+
     return result;
 }
 
@@ -265,4 +268,4 @@ void clearModel(NeuralNetwork *model)
         model->layers = NULL;
         model->numberOfLayers = 0;
     }
-}
\ No newline at end of file
+}