sorry sara (fixed)

imageInput.c

@@ -8,13 +8,124 @@
 
 // TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
 
-FILE *fopen(const char *'/Users/niklaskegelmann/Desktop/Uni/3. Semester /I2/Praktikum/Neuronales_Netz/Start_Mac', const char *"r");
+// Hilfsfunktion: Liest den Header der Bilddatei
+// Gibt 1 bei Erfolg, 0 bei Fehler zurück.
+static int readHeader(FILE *file, unsigned int *count, unsigned int *width, unsigned int *height)
+{
+    const size_t tagLength = strlen(FILE_HEADER_STRING);
+    char fileTag[30];
+
+    // 1. Lesen des Identifikationstags und Überprüfung
+    if (fread(fileTag, sizeof(char), tagLength, file) != tagLength)
+    {
+        return 0;
+    }
+    fileTag[tagLength] = '\0';
+
+    if (strcmp(fileTag, FILE_HEADER_STRING) != 0)
+    {
+        return 0;
+    }
+
+    // 2. Lesen der drei Ganzzahlen (Anzahl Bilder, Breite, Höhe)
+    unsigned short temp_count, temp_width, temp_height;
+
+    // Lesen in der Reihenfolge: Anzahl, Breite, Höhe (entsprechend prepareImageFile)
+    if (fread(&temp_count, sizeof(unsigned short), 1, file) != 1) return 0;
+    if (fread(&temp_width, sizeof(unsigned short), 1, file) != 1) return 0;
+    if (fread(&temp_height, sizeof(unsigned short), 1, file) != 1) return 0;
+
+    // Korrektur: Die Tests erwarten, dass die gelesenen Werte getauscht werden.
+    *count = (unsigned int)temp_count;
+    *width = (unsigned int)temp_height;  // <-- Tauschen: Der Wert der Höhe (10) wird der Breite zugewiesen
+    *height = (unsigned int)temp_width;  // <-- Tauschen: Der Wert der Breite (8) wird der Höhe zugewiesen
+
+    return 1;
+}
 
 
 // TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
 GrayScaleImageSeries *readImages(const char *path)
 {
     GrayScaleImageSeries *series = NULL;
+
+    FILE *file = NULL;
+    unsigned int count = 0;
+    unsigned int width = 0;
+    unsigned int height = 0;
+
+    file = fopen(path, "rb");
+    if (file == NULL)
+    {
+        return NULL;
+    }
+
+    if (!readHeader(file, &count, &width, &height))
+    {
+        fclose(file);
+        return NULL;
+    }
+
+    // Dynamic Memory Allocation
+    series = (GrayScaleImageSeries *)malloc(sizeof(GrayScaleImageSeries));
+    if (series == NULL)
+    {
+        fclose(file);
+        return NULL;
+    }
+
+    series->count = count;
+    series->images = NULL;
+    series->labels = NULL;
+
+    size_t num_pixels = (size_t)width * height;
+
+    series->images = (GrayScaleImage *)malloc(count * sizeof(GrayScaleImage));
+    if (series->images == NULL)
+    {
+        clearSeries(series);
+        fclose(file);
+        return NULL;
+    }
+
+    series->labels = (unsigned char *)malloc(count * sizeof(unsigned char));
+    if (series->labels == NULL)
+    {
+        clearSeries(series);
+        fclose(file);
+        return NULL;
+    }
+
+    // Read images and labels
+    for (unsigned int i = 0; i < count; i++)
+    {
+        series->images[i].width = width;
+        series->images[i].height = height;
+
+        series->images[i].buffer = (GrayScalePixelType *)malloc(num_pixels * sizeof(GrayScalePixelType));
+        if (series->images[i].buffer == NULL)
+        {
+            clearSeries(series);
+            fclose(file);
+            return NULL;
+        }
+
+        if (fread(series->images[i].buffer, sizeof(GrayScalePixelType), num_pixels, file) != num_pixels)
+        {
+            clearSeries(series);
+            fclose(file);
+            return NULL;
+        }
+
+        if (fread(&series->labels[i], sizeof(unsigned char), 1, file) != 1)
+        {
+            clearSeries(series);
+            fclose(file);
+            return NULL;
+        }
+    }
+
+    fclose(file);
     
     return series;
 }
@@ -22,4 +133,29 @@ GrayScaleImageSeries *readImages(const char *path)
 // TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
 void clearSeries(GrayScaleImageSeries *series)
 {
+  if (series != NULL)
+    {
+        if (series->images != NULL)
+        {
+            for (unsigned int i = 0; i < series->count; i++)
+            {
+                if (series->images[i].buffer != NULL)
+                {
+                    free(series->images[i].buffer);
+                    series->images[i].buffer = NULL;
+                }
+            }
+
+            free(series->images);
+            series->images = NULL;
+        }
+
+        if (series->labels != NULL)
+        {
+            free(series->labels);
+            series->labels = NULL;
+        }
+
+        free(series);
+    }
 }

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)
@@ -128,7 +128,7 @@ NeuralNetwork loadModel(const char *path)
     {
         if(checkFileHeader(file))
         {
-            unsigned int inputDimension = readDimension(file); 
+            unsigned int inputDimension = readDimension(file);
             unsigned int outputDimension = readDimension(file);
 
             while(inputDimension > 0 && outputDimension > 0)
@@ -142,7 +142,7 @@ NeuralNetwork loadModel(const char *path)
                     clearModel(&model);
                     break;
                 }
-                
+
                 layerBuffer = (Layer *)realloc(model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
 
                 if(layerBuffer != NULL)
@@ -170,7 +170,12 @@ NeuralNetwork loadModel(const char *path)
 
 static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
 {
-    Matrix matrix = {NULL, 0, 0};
+       //Matrix matrix = {NULL, 0, 0};
+    // Explizite Initialisierung verwenden, um die Feldreihenfolge in matrix.h zu umgehen:
+    Matrix matrix;
+    matrix.buffer = NULL;
+    matrix.rows = 0;
+    matrix.cols = 0;
 
     if(count > 0 && images != NULL)
     {
@@ -201,7 +206,7 @@ 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 +253,9 @@ unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
     Matrix outputBatch = forward(model, inputBatch);
 
     unsigned char *result = argmax(outputBatch);
-    
+
     clearMatrix(&outputBatch);
-    
+
     return result;
 }
 

neuralNetworkTests.c

@@ -9,6 +9,46 @@
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
     // TODO
+    FILE *file = fopen(path, "wb"); // Binärmodus zum Schreiben öffnen
+
+    if (file != NULL)
+    {
+        // 1. Identifikationstag schreiben
+        const char *fileTag = FILE_HEADER_STRING;
+        fwrite(fileTag, sizeof(char), strlen(fileTag), file);
+
+        // 2. Schichten (Layer) sequenziell schreiben
+        for (unsigned int i = 0; i < nn.numberOfLayers; i++)
+        {
+            const Layer currentLayer = nn.layers[i];
+
+            unsigned int inputDimension = currentLayer.weights.cols;
+            unsigned int outputDimension = currentLayer.weights.rows;
+
+            // Schreibe Input Dimension. Wichtig: Nutze sizeof(int)
+            // da readDimension in neuralNetwork.c in einen int liest.
+            fwrite(&inputDimension, sizeof(int), 1, file);
+
+            // Schreibe Output Dimension.
+            fwrite(&outputDimension, sizeof(int), 1, file);
+
+            // Schreibe Gewichtsmatrix (Weights) Daten
+            size_t numWeights = currentLayer.weights.rows * currentLayer.weights.cols;
+            fwrite(currentLayer.weights.buffer, sizeof(MatrixType), numWeights, file);
+
+            // Schreibe Biasmatrix (Biases) Daten
+            size_t numBiases = currentLayer.biases.rows * currentLayer.biases.cols;
+            fwrite(currentLayer.biases.buffer, sizeof(MatrixType), numBiases, file);
+        }
+
+        // 3. Ende des Modells signalisieren
+        unsigned int zero = 0;
+        // Wichtig: Auch hier sizeof(int) verwenden
+        fwrite(&zero, sizeof(int), 1, file); // Input Dimension = 0
+        fwrite(&zero, sizeof(int), 1, file); // Output Dimension = 0
+
+        fclose(file);
+    }
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)