copied files to windows and linux

I2_NeuronalerAbsturz/Start_Linux/imageInput.c

@@ -8,15 +8,116 @@
 
 // TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
 
+/* ---------------- Hilfsfunktionen ---------------- */
+
+static int readHeader(FILE *file, unsigned short *count, unsigned short *width, unsigned short *height)
+{
+    unsigned short headerlength = strlen(FILE_HEADER_STRING);
+    char buffer[headerlength + 1];
+    if (fread(buffer, 1, headerlength, file) != headerlength)
+        return 0;
+
+    buffer[headerlength] = '\0';
+
+    if (strcmp(buffer, FILE_HEADER_STRING) != 0)
+        return 0;
+
+    if (fread(count, sizeof(unsigned short), 1, file) != 1)
+        return 0;
+
+    if (fread(width, sizeof(unsigned short), 1, file) != 1)
+        return 0;
+
+    if (fread(height, sizeof(unsigned short), 1, file) != 1)
+        return 0;
+
+    return 1;
+}
+
+static int readSingleImage(FILE *file, GrayScaleImage *image)
+{
+    unsigned int pixelCount = image->width * image->height;
+
+    if (fread(image->buffer, sizeof(GrayScalePixelType), pixelCount, file) != pixelCount)
+        return 0;
+
+    return 1;
+}
+
+
 // TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
 GrayScaleImageSeries *readImages(const char *path)
 {
-    GrayScaleImageSeries *series = NULL;
-    
+    FILE *file = fopen(path, "rb");
+    if (!file) 
+        return NULL;
+
+    unsigned short count, width, height;
+
+    if (!readHeader(file, &count, &width, &height)) {
+        fclose(file);
+        return NULL;
+    }
+
+    GrayScaleImageSeries *series = malloc(sizeof(GrayScaleImageSeries));
+    series->count = count;
+    series->images = malloc(count * sizeof(GrayScaleImage));
+    series->labels = malloc(count * sizeof(unsigned char));
+
+    if (!series || !series->images || !series->labels) 
+    {
+        free(series->images);
+        free(series->labels);
+        free(series);
+        fclose(file);
+        return NULL;
+    }
+
+    for (unsigned int i = 0; i < count; i++)
+    {
+        series->images[i].width = width;
+        series->images[i].height = height;
+        series->images[i].buffer = malloc(width * height * sizeof(GrayScalePixelType));
+
+        //malloc prüfen
+        if (!series->images[i].buffer) 
+        {
+            clearSeries(series);
+            fclose(file);
+            return NULL;
+        }
+
+        //Image einlesen + prüfen
+        if (!readSingleImage(file, &series->images[i])) {
+            fclose(file);
+            clearSeries(series);
+            return NULL;
+        }
+
+        // label einlesen
+        if (fread(&series->labels[i], sizeof(unsigned char), 1, file) != 1) {
+            fclose(file);
+            clearSeries(series);
+            return NULL;
+        }
+    }
+
+    fclose(file);
     return series;
 }
 
 // TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
 void clearSeries(GrayScaleImageSeries *series)
 {
+    if(series)
+    {
+        for(unsigned int i = 0; i < series->count; i++)
+        {
+            free(series->images[i].buffer);
+        }
+
+        free(series->images);
+        free(series->labels);
+        free(series);
+    }
 }

I2_NeuronalerAbsturz/Start_Linux/imageInputTests.c

@@ -13,10 +13,15 @@ static void prepareImageFile(const char *path, unsigned short int width, unsigne
     if(file != NULL)
     {
         const char *fileTag = "__info2_image_file_format__";
-        GrayScalePixelType *zeroBuffer = (GrayScalePixelType *)calloc(numberOfImages * width * height, sizeof(GrayScalePixelType));
+        GrayScalePixelType *buffer = (GrayScalePixelType *)calloc(numberOfImages * width * height, sizeof(GrayScalePixelType));
 
-        if(zeroBuffer != NULL)
+        if(buffer != NULL)
         {
+            for(unsigned int i = 0; i < (numberOfImages * width * height); i++)
+            {
+                buffer[i] = (GrayScalePixelType)i;
+            }
+
             fwrite(fileTag, sizeof(fileTag[0]), strlen(fileTag), file);
             fwrite(&numberOfImages, sizeof(numberOfImages), 1, file);
             fwrite(&width, sizeof(width), 1, file);
@@ -24,11 +29,11 @@ static void prepareImageFile(const char *path, unsigned short int width, unsigne
 
             for(int i = 0; i < numberOfImages; i++)
             {
-                fwrite(zeroBuffer, sizeof(GrayScalePixelType), width * height, file);
+                fwrite(buffer, sizeof(GrayScalePixelType), width * height, file);
                 fwrite(&label, sizeof(unsigned char), 1, file);
             }
 
-            free(zeroBuffer);
+            free(buffer);
         }
 
         fclose(file);
@@ -54,7 +59,7 @@ void test_readImagesReturnsCorrectImageWidth(void)
     GrayScaleImageSeries *series = NULL;
     const unsigned short expectedWidth = 10;
     const char *path = "testFile.info2";
-    prepareImageFile(path, 8, expectedWidth, 2, 1);
+    prepareImageFile(path, expectedWidth, 8,  2, 1);
     series = readImages(path);
     TEST_ASSERT_NOT_NULL(series);
     TEST_ASSERT_NOT_NULL(series->images);
@@ -70,7 +75,7 @@ void test_readImagesReturnsCorrectImageHeight(void)
     GrayScaleImageSeries *series = NULL;
     const unsigned short expectedHeight = 10;
     const char *path = "testFile.info2";
-    prepareImageFile(path, expectedHeight, 8, 2, 1);
+    prepareImageFile(path, 8,  expectedHeight, 2, 1);
     series = readImages(path);
     TEST_ASSERT_NOT_NULL(series);
     TEST_ASSERT_NOT_NULL(series->images);
@@ -119,6 +124,27 @@ void test_readImagesFailsOnWrongFileTag(void)
     remove(path);
 }
 
+void test_readImagesReadsCorrectGrayScales(void)
+{
+    GrayScaleImageSeries *series = NULL;
+    const char *path = "testFile.info2";
+    
+    prepareImageFile(path, 8, 8, 1, 1);
+    series = readImages(path);
+
+    TEST_ASSERT_NOT_NULL(series);
+    TEST_ASSERT_NOT_NULL(series->images);
+    TEST_ASSERT_EQUAL_UINT16(1, series->count);
+
+    for (unsigned int i = 0; i < (8 * 8); i++)
+    {
+        TEST_ASSERT_EQUAL_UINT8((GrayScalePixelType)i, series->images->buffer[i]);
+    }
+
+    clearSeries(series);
+    remove(path);
+}
+
 void setUp(void) {
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
@@ -138,6 +164,7 @@ int main()
     RUN_TEST(test_readImagesReturnsCorrectLabels);
     RUN_TEST(test_readImagesReturnsNullOnNotExistingPath);
     RUN_TEST(test_readImagesFailsOnWrongFileTag);
+    RUN_TEST(test_readImagesReadsCorrectGrayScales);
 
     return UNITY_END();
 }

I2_NeuronalerAbsturz/Start_Linux/matrix.c

@@ -6,30 +6,146 @@
 
 Matrix createMatrix(unsigned int rows, unsigned int cols)
 {
+    Matrix m;
+
+    if (rows == 0 || cols == 0) 
+    {
+        m.rows = 0;
+        m.cols = 0;
+        m.buffer = NULL;
+        return m;
+    }
+
+    m.rows = rows;
+    m.cols = cols;
+    m.buffer = (MatrixType *)calloc(rows * cols, sizeof(MatrixType));
     
+    return m;
 }
 
 void clearMatrix(Matrix *matrix)
 {
-    
+    if (matrix != NULL)
+    {
+        if (matrix->buffer != NULL) 
+        {
+            free(matrix->buffer);
+            matrix->buffer = NULL;
+        }
+
+        matrix->rows = 0;
+        matrix->cols = 0;
+    }
 }
 
 void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-    
+    if (matrix.buffer != NULL)
+    {
+        if (rowIdx < matrix.rows && colIdx < matrix.cols)
+        {
+            matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
+        }
+    }
 }
 
 MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-    
+    if (matrix.buffer == NULL || rowIdx >= matrix.rows || colIdx >= matrix.cols)   
+    {
+        return UNDEFINED_MATRIX_VALUE;
+    }
+
+    return matrix.buffer[rowIdx * matrix.cols + colIdx];
 }
 
 Matrix add(const Matrix matrix1, const Matrix matrix2)
 {
-    
+    Matrix result;
+
+    if (matrix1.buffer == NULL || matrix2.buffer == NULL || matrix1.rows != matrix2.rows)
+    {
+        result.rows = 0;
+        result.cols = 0;
+        result.buffer = NULL;
+        return result;
+    }
+
+    if (matrix1.cols == matrix2.cols)
+    {
+        result = createMatrix(matrix1.rows, matrix1.cols);
+        for (int i = 0; i < matrix1.rows; i++)
+        {
+            for (int j = 0; j < matrix1.cols; j++)
+            {
+                MatrixType value = getMatrixAt(matrix1, i, j) + getMatrixAt(matrix2, i, j);
+                setMatrixAt(value, result, i, j);
+            }
+        }
+        return result;
+    }
+
+    if (matrix1.cols == 1 && matrix2.cols > 1)
+    {
+        result = createMatrix(matrix1.rows, matrix2.cols);
+        for (int i = 0; i < matrix1.rows; i++)
+        {
+            for (int j = 0; j < matrix2.cols; j++)
+            {
+                MatrixType value = getMatrixAt(matrix1, i, 0) + getMatrixAt(matrix2, i, j);
+                setMatrixAt(value, result, i, j);
+            }
+        }
+        return result;
+    }
+    else if (matrix2.cols == 1 && matrix1.cols > 1)
+    {
+        result = createMatrix(matrix1.rows, matrix1.cols);
+        for (int i = 0; i < matrix1.rows; i++)
+        {
+            for (int j = 0; j < matrix1.cols; j++)
+            {
+                MatrixType value = getMatrixAt(matrix1, i, j) + getMatrixAt(matrix2, i, 0);
+                setMatrixAt(value, result, i, j);
+            }
+        }
+        return result;
+    }
+
+    //Fall: Unterschiedliche Spaltenanzahl, beide ungleich 1
+    result.rows = 0;
+    result.cols = 0;
+    result.buffer = NULL;
+    return result;
 }
 
 Matrix multiply(const Matrix matrix1, const Matrix matrix2)
 {
-    
-}
+    Matrix result;
+
+    if (matrix1.buffer == NULL || matrix2.buffer == NULL || matrix1.cols != matrix2.rows) 
+    {
+        result.rows = 0;
+        result.cols = 0;
+        result.buffer = NULL;
+        return result;
+    }
+
+    result = createMatrix(matrix1.rows, matrix2.cols);
+
+    for (int i = 0; i < matrix1.rows; i++) 
+    {
+        for (int j = 0; j < matrix2.cols; j++) 
+        {
+            MatrixType sum = 0;
+
+            for (int k = 0; k < matrix1.cols; k++) 
+            {
+                sum += getMatrixAt(matrix1, i, k) * getMatrixAt(matrix2, k, j);
+            }
+            setMatrixAt(sum, result, i, j);
+        }
+    }
+
+    return result;
+}

I2_NeuronalerAbsturz/Start_Linux/matrix.h

@@ -7,6 +7,13 @@ typedef float MatrixType;
 
 // TODO Matrixtyp definieren
 
+typedef struct
+{
+    MatrixType *buffer;
+    int rows;
+    int cols;
+} Matrix;
+
 
 Matrix createMatrix(unsigned int rows, unsigned int cols);
 void clearMatrix(Matrix *matrix);

I2_NeuronalerAbsturz/Start_Linux/neuralNetworkTests.c

@@ -5,12 +5,80 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
+/* 
+################
+Aufbau Test File 
+################
+
+
+HEADER
+
+inputDim
+outputDim
+
+-- Layer 1 --
+weights
+biases
+
+outputDim
+
+-- Layer 2 --
+weights
+biases
+
+...
+...
+-- Layer n --
+weights
+biases
+
+outputDim = 0  => Ende 
+*/
+
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    FILE *file = fopen(path, "wb");
+    if (file)
+    {
+        const char *fileTag = "__info2_neural_network_file_format__";
+        fwrite(fileTag, 1, strlen(fileTag), file);
+
+        //Stopt loadModel, falls keine Layer vorhanden
+        if (nn.numberOfLayers == 0) 
+        {
+            int zero = 0;
+            fwrite(&zero, sizeof(int), 1, file);
+            fclose(file);
+            return;
+        }
+
+        // input und output dimension schreiben
+        int inputDim = nn.layers[0].weights.cols;
+        fwrite(&inputDim, sizeof(int), 1, file);
+
+        // für weiter Layer nur outputDimension schreiben
+        for (unsigned int i = 0; i < nn.numberOfLayers; i++)
+        {
+            int outputDim = nn.layers[i].weights.rows;
+            fwrite(&outputDim, sizeof(int), 1, file);
+
+            int weightCount = nn.layers[i].weights.rows * nn.layers[i].weights.cols;
+            fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), weightCount, file);
+
+            int biasesCount = nn.layers[i].biases.rows * nn.layers[i].biases.cols;
+            fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), biasesCount, file);
+        }
+
+        // loadModel ließt 0 ein -> Stop
+        int fileEnd = 0;
+        fwrite(&fileEnd, sizeof(int), 1, file);
 }
 
+    fclose(file);
+}
+
+
 void test_loadModelReturnsCorrectNumberOfLayers(void)
 {
     const char *path = "some__nn_test_file.info2";

I2_NeuronalerAbsturz/Start_Mac/neuralNetworkTests.c

@@ -17,8 +17,8 @@ inputDim
 outputDim
 
 -- Layer 1 --
-weights (outputDim * inputDim * MatrixType)
-biases  (outputDim * MatrixType)
+weights
+biases
 
 outputDim
 

I2_NeuronalerAbsturz/Start_Windows/imageInput.c

@@ -8,15 +8,116 @@
 
 // TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
 
+/* ---------------- Hilfsfunktionen ---------------- */
+
+static int readHeader(FILE *file, unsigned short *count, unsigned short *width, unsigned short *height)
+{
+    unsigned short headerlength = strlen(FILE_HEADER_STRING);
+    char buffer[headerlength + 1];
+    if (fread(buffer, 1, headerlength, file) != headerlength)
+        return 0;
+
+    buffer[headerlength] = '\0';
+
+    if (strcmp(buffer, FILE_HEADER_STRING) != 0)
+        return 0;
+
+    if (fread(count, sizeof(unsigned short), 1, file) != 1)
+        return 0;
+
+    if (fread(width, sizeof(unsigned short), 1, file) != 1)
+        return 0;
+
+    if (fread(height, sizeof(unsigned short), 1, file) != 1)
+        return 0;
+
+    return 1;
+}
+
+static int readSingleImage(FILE *file, GrayScaleImage *image)
+{
+    unsigned int pixelCount = image->width * image->height;
+
+    if (fread(image->buffer, sizeof(GrayScalePixelType), pixelCount, file) != pixelCount)
+        return 0;
+
+    return 1;
+}
+
+
 // TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
 GrayScaleImageSeries *readImages(const char *path)
 {
-    GrayScaleImageSeries *series = NULL;
-    
+    FILE *file = fopen(path, "rb");
+    if (!file) 
+        return NULL;
+
+    unsigned short count, width, height;
+
+    if (!readHeader(file, &count, &width, &height)) {
+        fclose(file);
+        return NULL;
+    }
+
+    GrayScaleImageSeries *series = malloc(sizeof(GrayScaleImageSeries));
+    series->count = count;
+    series->images = malloc(count * sizeof(GrayScaleImage));
+    series->labels = malloc(count * sizeof(unsigned char));
+
+    if (!series || !series->images || !series->labels) 
+    {
+        free(series->images);
+        free(series->labels);
+        free(series);
+        fclose(file);
+        return NULL;
+    }
+
+    for (unsigned int i = 0; i < count; i++)
+    {
+        series->images[i].width = width;
+        series->images[i].height = height;
+        series->images[i].buffer = malloc(width * height * sizeof(GrayScalePixelType));
+
+        //malloc prüfen
+        if (!series->images[i].buffer) 
+        {
+            clearSeries(series);
+            fclose(file);
+            return NULL;
+        }
+
+        //Image einlesen + prüfen
+        if (!readSingleImage(file, &series->images[i])) {
+            fclose(file);
+            clearSeries(series);
+            return NULL;
+        }
+
+        // label einlesen
+        if (fread(&series->labels[i], sizeof(unsigned char), 1, file) != 1) {
+            fclose(file);
+            clearSeries(series);
+            return NULL;
+        }
+    }
+
+    fclose(file);
     return series;
 }
 
 // TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
 void clearSeries(GrayScaleImageSeries *series)
 {
+    if(series)
+    {
+        for(unsigned int i = 0; i < series->count; i++)
+        {
+            free(series->images[i].buffer);
+        }
+
+        free(series->images);
+        free(series->labels);
+        free(series);
+    }
 }

I2_NeuronalerAbsturz/Start_Windows/matrix.c

@@ -6,30 +6,146 @@
 
 Matrix createMatrix(unsigned int rows, unsigned int cols)
 {
+    Matrix m;
+
+    if (rows == 0 || cols == 0) 
+    {
+        m.rows = 0;
+        m.cols = 0;
+        m.buffer = NULL;
+        return m;
+    }
+
+    m.rows = rows;
+    m.cols = cols;
+    m.buffer = (MatrixType *)calloc(rows * cols, sizeof(MatrixType));
     
+    return m;
 }
 
 void clearMatrix(Matrix *matrix)
 {
-    
+    if (matrix != NULL)
+    {
+        if (matrix->buffer != NULL) 
+        {
+            free(matrix->buffer);
+            matrix->buffer = NULL;
+        }
+
+        matrix->rows = 0;
+        matrix->cols = 0;
+    }
 }
 
 void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-    
+    if (matrix.buffer != NULL)
+    {
+        if (rowIdx < matrix.rows && colIdx < matrix.cols)
+        {
+            matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
+        }
+    }
 }
 
 MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-    
+    if (matrix.buffer == NULL || rowIdx >= matrix.rows || colIdx >= matrix.cols)   
+    {
+        return UNDEFINED_MATRIX_VALUE;
+    }
+
+    return matrix.buffer[rowIdx * matrix.cols + colIdx];
 }
 
 Matrix add(const Matrix matrix1, const Matrix matrix2)
 {
-    
+    Matrix result;
+
+    if (matrix1.buffer == NULL || matrix2.buffer == NULL || matrix1.rows != matrix2.rows)
+    {
+        result.rows = 0;
+        result.cols = 0;
+        result.buffer = NULL;
+        return result;
+    }
+
+    if (matrix1.cols == matrix2.cols)
+    {
+        result = createMatrix(matrix1.rows, matrix1.cols);
+        for (int i = 0; i < matrix1.rows; i++)
+        {
+            for (int j = 0; j < matrix1.cols; j++)
+            {
+                MatrixType value = getMatrixAt(matrix1, i, j) + getMatrixAt(matrix2, i, j);
+                setMatrixAt(value, result, i, j);
+            }
+        }
+        return result;
+    }
+
+    if (matrix1.cols == 1 && matrix2.cols > 1)
+    {
+        result = createMatrix(matrix1.rows, matrix2.cols);
+        for (int i = 0; i < matrix1.rows; i++)
+        {
+            for (int j = 0; j < matrix2.cols; j++)
+            {
+                MatrixType value = getMatrixAt(matrix1, i, 0) + getMatrixAt(matrix2, i, j);
+                setMatrixAt(value, result, i, j);
+            }
+        }
+        return result;
+    }
+    else if (matrix2.cols == 1 && matrix1.cols > 1)
+    {
+        result = createMatrix(matrix1.rows, matrix1.cols);
+        for (int i = 0; i < matrix1.rows; i++)
+        {
+            for (int j = 0; j < matrix1.cols; j++)
+            {
+                MatrixType value = getMatrixAt(matrix1, i, j) + getMatrixAt(matrix2, i, 0);
+                setMatrixAt(value, result, i, j);
+            }
+        }
+        return result;
+    }
+
+    //Fall: Unterschiedliche Spaltenanzahl, beide ungleich 1
+    result.rows = 0;
+    result.cols = 0;
+    result.buffer = NULL;
+    return result;
 }
 
 Matrix multiply(const Matrix matrix1, const Matrix matrix2)
 {
-    
-}
+    Matrix result;
+
+    if (matrix1.buffer == NULL || matrix2.buffer == NULL || matrix1.cols != matrix2.rows) 
+    {
+        result.rows = 0;
+        result.cols = 0;
+        result.buffer = NULL;
+        return result;
+    }
+
+    result = createMatrix(matrix1.rows, matrix2.cols);
+
+    for (int i = 0; i < matrix1.rows; i++) 
+    {
+        for (int j = 0; j < matrix2.cols; j++) 
+        {
+            MatrixType sum = 0;
+
+            for (int k = 0; k < matrix1.cols; k++) 
+            {
+                sum += getMatrixAt(matrix1, i, k) * getMatrixAt(matrix2, k, j);
+            }
+            setMatrixAt(sum, result, i, j);
+        }
+    }
+
+    return result;
+}

I2_NeuronalerAbsturz/Start_Windows/matrix.h

@@ -7,6 +7,13 @@ typedef float MatrixType;
 
 // TODO Matrixtyp definieren
 
+typedef struct
+{
+    MatrixType *buffer;
+    int rows;
+    int cols;
+} Matrix;
+
 
 Matrix createMatrix(unsigned int rows, unsigned int cols);
 void clearMatrix(Matrix *matrix);

I2_NeuronalerAbsturz/Start_Windows/neuralNetworkTests.c

@@ -5,12 +5,80 @@
 #include "unity.h"
 #include "neuralNetwork.h"
 
+/* 
+################
+Aufbau Test File 
+################
+
+
+HEADER
+
+inputDim
+outputDim
+
+-- Layer 1 --
+weights
+biases
+
+outputDim
+
+-- Layer 2 --
+weights
+biases
+
+...
+...
+-- Layer n --
+weights
+biases
+
+outputDim = 0  => Ende 
+*/
+
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    FILE *file = fopen(path, "wb");
+    if (file)
+    {
+        const char *fileTag = "__info2_neural_network_file_format__";
+        fwrite(fileTag, 1, strlen(fileTag), file);
+
+        //Stopt loadModel, falls keine Layer vorhanden
+        if (nn.numberOfLayers == 0) 
+        {
+            int zero = 0;
+            fwrite(&zero, sizeof(int), 1, file);
+            fclose(file);
+            return;
+        }
+
+        // input und output dimension schreiben
+        int inputDim = nn.layers[0].weights.cols;
+        fwrite(&inputDim, sizeof(int), 1, file);
+
+        // für weiter Layer nur outputDimension schreiben
+        for (unsigned int i = 0; i < nn.numberOfLayers; i++)
+        {
+            int outputDim = nn.layers[i].weights.rows;
+            fwrite(&outputDim, sizeof(int), 1, file);
+
+            int weightCount = nn.layers[i].weights.rows * nn.layers[i].weights.cols;
+            fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), weightCount, file);
+
+            int biasesCount = nn.layers[i].biases.rows * nn.layers[i].biases.cols;
+            fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), biasesCount, file);
+        }
+
+        // loadModel ließt 0 ein -> Stop
+        int fileEnd = 0;
+        fwrite(&fileEnd, sizeof(int), 1, file);
 }
 
+    fclose(file);
+}
+
+
 void test_loadModelReturnsCorrectNumberOfLayers(void)
 {
     const char *path = "some__nn_test_file.info2";