updating

.gitignore

@@ -1,4 +1,61 @@
-mnist
-runTests
+# ---> C
+# Prerequisites
+*.d
+
+# Object files
 *.o
-*.exe
+*.ko
+*.obj
+*.elf
+
+# Linker output
+*.ilk
+*.map
+*.exp
+
+# Precompiled Headers
+*.gch
+*.pch
+
+# Libraries
+*.lib
+*.la
+*.lo
+
+# Shared objects (inc. Windows DLLs)
+*.dll
+*.so
+*.so.*
+*.dylib
+
+# Executables
+*.exe
+*.out
+*.app
+*.i*86
+*.x86_64
+*.hex
+Startcode/mnist
+Startcode/runTests
+
+# Debug files
+*.dSYM/
+*.su
+*.idb
+*.pdb
+
+# Kernel Module Compile Results
+*.mod*
+*.cmd
+.tmp_versions/
+modules.order
+Module.symvers
+Mkfile.old
+dkms.conf
+
+# IDE folders
+.vscode/
+.idea/
+
+# macOS
+.DS_Store

.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-    "makefile.configureOnOpen": false
-}

README.md

@@ -1,2 +0,0 @@
-# Projekt 2 für Informatik 2 Praktikum
-  

Startcode/imageInput.c

@@ -7,23 +7,16 @@
 #define FILE_HEADER_STRING "__info2_image_file_format__"
 
 // TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
-GrayScaleImage readImage()
-{
-
-}
 
 // TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
 GrayScaleImageSeries *readImages(const char *path)
 {
     GrayScaleImageSeries *series = NULL;
-    FILE *file = fopen("mnist_test.info2","rb");
-    char headOfFile;
-    series = malloc();
+    
     return series;
 }
 
 // TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
 void clearSeries(GrayScaleImageSeries *series)
 {
-
 }

Startcode/makefile

@@ -62,4 +62,5 @@ ifeq ($(OS),Windows_NT)
 	del /f *.o *.exe
 else
 	rm -f *.o mnist runMatrixTests runNeuralNetworkTests runImageInputTests
-endif
+endif
+

Startcode/matrix.c

@@ -0,0 +1,35 @@
+#include <stdlib.h>
+#include <string.h>
+#include "matrix.h"
+
+// TODO Matrix-Funktionen implementieren
+
+Matrix createMatrix(unsigned int rows, unsigned int cols)
+{
+    
+}
+
+void clearMatrix(Matrix *matrix)
+{
+    
+}
+
+void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
+{
+    
+}
+
+MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
+{
+    
+}
+
+Matrix add(const Matrix matrix1, const Matrix matrix2)
+{
+    
+}
+
+Matrix multiply(const Matrix matrix1, const Matrix matrix2)
+{
+    
+}

Startcode/matrix.h

@@ -6,25 +6,14 @@
 typedef float MatrixType;
 
 // TODO Matrixtyp definieren
-typedef struct {
-  unsigned int rows;
-  unsigned int cols;
-  MatrixType *buffer;
 
-} Matrix;
 
 Matrix createMatrix(unsigned int rows, unsigned int cols);
 void clearMatrix(Matrix *matrix);
-void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx,
-                 unsigned int colIdx);
-MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx,
-                       unsigned int colIdx);
-
-Matrix broadCastCols(const Matrix matrix, const unsigned int rows,
-                     const unsigned int cols);
-Matrix broadCastRows(const Matrix matrix, const unsigned int rows,
-                     const unsigned int cols);
+void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx);
+MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx);
 Matrix add(const Matrix matrix1, const Matrix matrix2);
 Matrix multiply(const Matrix matrix1, const Matrix matrix2);
 
+
 #endif

Startcode/neuralNetwork.c

@@ -0,0 +1,268 @@
+#include <stdlib.h>
+#include <stdio.h>
+#include <math.h>
+#include <string.h>
+#include "neuralNetwork.h"
+
+#define BUFFER_SIZE 100
+#define FILE_HEADER_STRING "__info2_neural_network_file_format__"
+
+static void softmax(Matrix *matrix)
+{
+    if(matrix->cols > 0)
+    {
+        double *colSums = (double *)calloc(matrix->cols, sizeof(double));
+
+        if(colSums != NULL)
+        {
+            for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
+            {
+                for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
+                {
+                    MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx));
+                    setMatrixAt(expValue, *matrix, rowIdx, colIdx);
+                    colSums[colIdx] += expValue;
+                }
+            }
+
+            for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
+            {
+                for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
+                {
+                    MatrixType normalizedValue = getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx];
+                    setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx);
+                }
+            }
+            free(colSums);
+        }
+    }
+}
+
+static void relu(Matrix *matrix)
+{
+    for(int i = 0; i < matrix->rows * matrix->cols; i++)
+    {
+        matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0;
+    }
+}
+
+static int checkFileHeader(FILE *file)
+{
+    int isValid = 0;
+    int fileHeaderLen = strlen(FILE_HEADER_STRING);
+    char buffer[BUFFER_SIZE] = {0};
+
+    if(BUFFER_SIZE-1 < fileHeaderLen)
+        fileHeaderLen = BUFFER_SIZE-1;
+
+    if(fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen)
+        isValid = strcmp(buffer, FILE_HEADER_STRING) == 0;
+
+    return isValid;
+}
+
+static unsigned int readDimension(FILE *file)
+{
+    int dimension = 0;
+
+    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)
+            clearMatrix(&matrix);
+    }
+
+    return matrix;
+}
+
+static Layer readLayer(FILE *file, unsigned int inputDimension, unsigned int outputDimension)
+{
+    Layer layer;
+    layer.weights = readMatrix(file, outputDimension, inputDimension);
+    layer.biases = readMatrix(file, outputDimension, 1);
+
+    return layer;
+}
+
+static int isEmptyLayer(const Layer layer)
+{
+    return layer.biases.cols == 0 || layer.biases.rows == 0 || layer.biases.buffer == NULL || layer.weights.rows == 0 || layer.weights.cols == 0 || layer.weights.buffer == NULL;
+}
+
+static void clearLayer(Layer *layer)
+{
+    if(layer != NULL)
+    {
+        clearMatrix(&layer->weights);
+        clearMatrix(&layer->biases);
+        layer->activation = NULL;
+    }
+}
+
+static void assignActivations(NeuralNetwork model)
+{
+    for(int i = 0; i < (int)model.numberOfLayers-1; i++)
+    {
+        model.layers[i].activation = relu;
+    }
+
+    if(model.numberOfLayers > 0)
+        model.layers[model.numberOfLayers-1].activation = softmax;
+}
+
+NeuralNetwork loadModel(const char *path)
+{
+    NeuralNetwork model = {NULL, 0};
+    FILE *file = fopen(path, "rb");
+
+    if(file != NULL)
+    {
+        if(checkFileHeader(file))
+        {
+            unsigned int inputDimension = readDimension(file); 
+            unsigned int outputDimension = readDimension(file);
+
+            while(inputDimension > 0 && outputDimension > 0)
+            {
+                Layer layer = readLayer(file, inputDimension, outputDimension);
+                Layer *layerBuffer = NULL;
+
+                if(isEmptyLayer(layer))
+                {
+                    clearLayer(&layer);
+                    clearModel(&model);
+                    break;
+                }
+                
+                layerBuffer = (Layer *)realloc(model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
+
+                if(layerBuffer != NULL)
+                    model.layers = layerBuffer;
+                else
+                {
+                    clearModel(&model);
+                    break;
+                }
+
+                model.layers[model.numberOfLayers] = layer;
+                model.numberOfLayers++;
+
+                inputDimension = outputDimension;
+                outputDimension = readDimension(file);
+            }
+        }
+        fclose(file);
+
+        assignActivations(model);
+    }
+
+    return model;
+}
+
+static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
+{
+    Matrix matrix = {NULL, 0, 0};
+
+    if(count > 0 && images != NULL)
+    {
+        matrix = createMatrix(images[0].height * images[0].width, count);
+
+        if(matrix.buffer != NULL)
+        {
+            for(int i = 0; i < count; i++)
+            {
+                for(int j = 0; j < images[i].width * images[i].height; j++)
+                {
+                    setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i);
+                }
+            }
+        }
+    }
+
+    return matrix;
+}
+
+static Matrix forward(const NeuralNetwork model, Matrix inputBatch)
+{
+    Matrix result = inputBatch;
+
+    if(result.buffer != NULL)
+    {
+        for(int i = 0; i < model.numberOfLayers; i++)
+        {
+            Matrix biasResult;
+            Matrix weightResult;
+            
+            weightResult = multiply(model.layers[i].weights, result);
+            clearMatrix(&result);
+            biasResult = add(model.layers[i].biases, weightResult);
+            clearMatrix(&weightResult);
+
+            if(model.layers[i].activation != NULL)
+                model.layers[i].activation(&biasResult);
+            result = biasResult;
+        }
+    }
+
+    return result;
+}
+
+unsigned char *argmax(const Matrix matrix)
+{
+    unsigned char *maxIdx = NULL;
+
+    if(matrix.rows > 0 && matrix.cols > 0)
+    {
+        maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols);
+
+        if(maxIdx != NULL)
+        {
+            for(int colIdx = 0; colIdx < matrix.cols; colIdx++)
+            {
+                maxIdx[colIdx] = 0;
+
+                for(int rowIdx = 1; rowIdx < matrix.rows; rowIdx++)
+                {
+                    if(getMatrixAt(matrix, rowIdx, colIdx) > getMatrixAt(matrix, maxIdx[colIdx], colIdx))
+                        maxIdx[colIdx] = rowIdx;
+                }
+            }
+        }
+    }
+
+    return maxIdx;
+}
+
+unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[], unsigned int numberOfImages)
+{
+    Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages);
+    Matrix outputBatch = forward(model, inputBatch);
+
+    unsigned char *result = argmax(outputBatch);
+    
+    clearMatrix(&outputBatch);
+    
+    return result;
+}
+
+void clearModel(NeuralNetwork *model)
+{
+    if(model != NULL)
+    {
+        for(int i = 0; i < model->numberOfLayers; i++)
+        {
+            clearLayer(&model->layers[i]);
+        }
+        model->layers = NULL;
+        model->numberOfLayers = 0;
+    }
+}

Startcode/neuralNetworkTests.c

@@ -0,0 +1,242 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <math.h>
+#include "unity.h"
+#include "neuralNetwork.h"
+
+
+static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
+{
+    // TODO
+}
+
+void test_loadModelReturnsCorrectNumberOfLayers(void)
+{
+    const char *path = "some__nn_test_file.info2";
+    MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
+    MatrixType buffer2[] = {1, 2, 3, 4, 5, 6};
+    Matrix weights1 = {.buffer=buffer1, .rows=3, .cols=2};
+    Matrix weights2 = {.buffer=buffer2, .rows=2, .cols=3};
+    MatrixType buffer3[] = {1, 2, 3};
+    MatrixType buffer4[] = {1, 2};
+    Matrix biases1 = {.buffer=buffer3, .rows=3, .cols=1};
+    Matrix biases2 = {.buffer=buffer4, .rows=2, .cols=1};
+    Layer layers[] = {{.weights=weights1, .biases=biases1}, {.weights=weights2, .biases=biases2}};
+
+    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=2};
+    NeuralNetwork netUnderTest;
+
+    prepareNeuralNetworkFile(path, expectedNet);
+
+    netUnderTest = loadModel(path);
+    remove(path);
+
+    TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers, netUnderTest.numberOfLayers);
+    clearModel(&netUnderTest);
+}
+
+void test_loadModelReturnsCorrectWeightDimensions(void)
+{
+    const char *path = "some__nn_test_file.info2";
+    MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
+    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    MatrixType biasBuffer[] = {7, 8, 9};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
+    Layer layers[] = {{.weights=weights, .biases=biases}};
+
+    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork netUnderTest;
+
+    prepareNeuralNetworkFile(path, expectedNet);
+
+    netUnderTest = loadModel(path);
+    remove(path);
+
+    TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
+    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows);
+    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols);
+    clearModel(&netUnderTest);
+}
+
+void test_loadModelReturnsCorrectBiasDimensions(void)
+{
+    const char *path = "some__nn_test_file.info2";
+    MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
+    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    MatrixType biasBuffer[] = {7, 8, 9};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
+    Layer layers[] = {{.weights=weights, .biases=biases}};
+
+    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork netUnderTest;
+
+    prepareNeuralNetworkFile(path, expectedNet);
+
+    netUnderTest = loadModel(path);
+    remove(path);
+
+    TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
+    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.rows, netUnderTest.layers[0].biases.rows);
+    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.cols, netUnderTest.layers[0].biases.cols);
+    clearModel(&netUnderTest);
+}
+
+void test_loadModelReturnsCorrectWeights(void)
+{
+    const char *path = "some__nn_test_file.info2";
+    MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
+    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    MatrixType biasBuffer[] = {7, 8, 9};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
+    Layer layers[] = {{.weights=weights, .biases=biases}};
+
+    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork netUnderTest;
+
+    prepareNeuralNetworkFile(path, expectedNet);
+
+    netUnderTest = loadModel(path);
+    remove(path);
+
+    TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
+    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows);
+    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols);
+    int n = netUnderTest.layers[0].weights.rows * netUnderTest.layers[0].weights.cols;
+    TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].weights.buffer, netUnderTest.layers[0].weights.buffer, n);
+    clearModel(&netUnderTest);
+}
+
+void test_loadModelReturnsCorrectBiases(void)
+{
+    const char *path = "some__nn_test_file.info2";
+    MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
+    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    MatrixType biasBuffer[] = {7, 8, 9};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
+    Layer layers[] = {{.weights=weights, .biases=biases}};
+
+    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork netUnderTest;
+
+    prepareNeuralNetworkFile(path, expectedNet);
+
+    netUnderTest = loadModel(path);
+    remove(path);
+
+    TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
+    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows);
+    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols);
+    int n = netUnderTest.layers[0].biases.rows * netUnderTest.layers[0].biases.cols;
+    TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].biases.buffer, netUnderTest.layers[0].biases.buffer, n);
+    clearModel(&netUnderTest);
+}
+
+void test_loadModelFailsOnWrongFileTag(void)
+{
+    const char *path = "some_nn_test_file.info2";
+    NeuralNetwork netUnderTest;
+    FILE *file = fopen(path, "wb");
+
+    if(file != NULL)
+    {
+        const char *fileTag = "info2_neural_network_file_format";
+
+        fwrite(fileTag, sizeof(char), strlen(fileTag), file);
+
+        fclose(file);
+    }
+
+    netUnderTest = loadModel(path);
+
+    remove(path);
+
+    TEST_ASSERT_NULL(netUnderTest.layers);
+    TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
+}
+
+void test_clearModelSetsMembersToNull(void)
+{
+    const char *path = "some__nn_test_file.info2";
+    MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
+    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    MatrixType biasBuffer[] = {7, 8, 9};
+    Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1};
+    Layer layers[] = {{.weights=weights, .biases=biases}};
+
+    NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
+    NeuralNetwork netUnderTest;
+
+    prepareNeuralNetworkFile(path, expectedNet);
+
+    netUnderTest = loadModel(path);
+    remove(path);
+
+    TEST_ASSERT_NOT_NULL(netUnderTest.layers);
+    TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
+    clearModel(&netUnderTest);
+    TEST_ASSERT_NULL(netUnderTest.layers);
+    TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
+}
+
+static void someActivation(Matrix *matrix)
+{
+    for(int i = 0; i < matrix->rows * matrix->cols; i++)
+    {
+        matrix->buffer[i] = fabs(matrix->buffer[i]);
+    }
+}
+
+void test_predictReturnsCorrectLabels(void)
+{
+    const unsigned char expectedLabels[] = {4, 2};
+    GrayScalePixelType imageBuffer1[] = {10, 30, 25, 17};
+    GrayScalePixelType imageBuffer2[] = {20, 40, 10, 128};
+    GrayScaleImage inputImages[] = {{.buffer=imageBuffer1, .width=2, .height=2}, {.buffer=imageBuffer2, .width=2, .height=2}};
+    MatrixType weightsBuffer1[] = {1, -2, 3, -4, 5, -6, 7, -8};
+    MatrixType weightsBuffer2[] = {-9, 10, 11, 12, 13, 14};
+    MatrixType weightsBuffer3[] = {-15, 16, 17, 18, -19, 20, 21, 22, 23, -24, 25, 26, 27, -28, -29};
+    Matrix weights1 = {.buffer=weightsBuffer1, .rows=2, .cols=4};
+    Matrix weights2 = {.buffer=weightsBuffer2, .rows=3, .cols=2};
+    Matrix weights3 = {.buffer=weightsBuffer3, .rows=5, .cols=3};
+    MatrixType biasBuffer1[] = {200, 0};
+    MatrixType biasBuffer2[] = {0, -100, 0};
+    MatrixType biasBuffer3[] = {0, -1000, 0, 2000, 0};
+    Matrix biases1 = {.buffer=biasBuffer1, .rows=2, .cols=1};
+    Matrix biases2 = {.buffer=biasBuffer2, .rows=3, .cols=1};
+    Matrix biases3 = {.buffer=biasBuffer3, .rows=5, .cols=1};
+    Layer layers[] = {{.weights=weights1, .biases=biases1, .activation=someActivation}, \
+                      {.weights=weights2, .biases=biases2, .activation=someActivation}, \
+                      {.weights=weights3, .biases=biases3, .activation=someActivation}};
+    NeuralNetwork netUnderTest = {.layers=layers, .numberOfLayers=3};
+    unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2);
+    TEST_ASSERT_NOT_NULL(predictedLabels);
+    int n = (int)(sizeof(expectedLabels) / sizeof(expectedLabels[0]));
+    TEST_ASSERT_EQUAL_UINT8_ARRAY(expectedLabels, predictedLabels, n);
+    free(predictedLabels);
+}
+
+void setUp(void) {
+    // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
+}
+
+void tearDown(void) {
+    // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
+}
+
+int main()
+{
+    UNITY_BEGIN();
+
+    printf("\n============================\nNeural network tests\n============================\n");
+    RUN_TEST(test_loadModelReturnsCorrectNumberOfLayers);
+    RUN_TEST(test_loadModelReturnsCorrectWeightDimensions);
+    RUN_TEST(test_loadModelReturnsCorrectBiasDimensions);
+    RUN_TEST(test_loadModelReturnsCorrectWeights);
+    RUN_TEST(test_loadModelReturnsCorrectBiases);
+    RUN_TEST(test_loadModelFailsOnWrongFileTag);
+    RUN_TEST(test_clearModelSetsMembersToNull);
+    RUN_TEST(test_predictReturnsCorrectLabels);
+
+    return UNITY_END();
+}

matrix.c

@@ -1,248 +0,0 @@
-#include "matrix.h"
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-
-// TODO Matrix-Funktionen implementieren
-/*typedef struct {
-    unsigned int rows;  //Zeilen
-    unsigned int cols; //Spalten
-    MatrixType *buffer; //Zeiger auf Speicherbereich Reihen*Spalten
-} Matrix;*/
-Matrix createMatrix(unsigned int rows, unsigned int cols) {
-
-  Matrix errorMatrix = {0, 0, NULL};
-  if (rows == 0 || cols == 0) {
-
-    return errorMatrix;
-  }
-  MatrixType *buffer =
-      malloc(rows * cols * sizeof(MatrixType)); // Speicher reservieren, malloc
-                                                // liefert Zeiger auf Speicher
-  Matrix newMatrix = {rows, cols, buffer};      // neue Matrix nach struct
-  return newMatrix;
-}
-void clearMatrix(Matrix *matrix) {
-  matrix->buffer = UNDEFINED_MATRIX_VALUE;
-  matrix->rows = UNDEFINED_MATRIX_VALUE;
-  matrix->cols = UNDEFINED_MATRIX_VALUE;
-  free((*matrix).buffer); // Speicher freigeben
-}
-
-void setMatrixAt(const MatrixType value, Matrix matrix,
-                 const unsigned int rowIdx, // Kopie der Matrix wird übergeben
-                 const unsigned int colIdx) {
-
-  if (rowIdx >= matrix.rows ||
-      colIdx >= matrix.cols) { // Speichergröße nicht überschreiten
-    return;
-  }
-
-  matrix.buffer[rowIdx * matrix.cols + colIdx] =
-      value; // rowIdx * matrix.cols -> Beginn der Zeile colIdx ->Spalte
-             // innerhalb der Zeile
-}
-MatrixType getMatrixAt(const Matrix matrix,
-                       unsigned int rowIdx, // Kopie der Matrix wird übergeben
-                       unsigned int colIdx) {
-  if (rowIdx >= matrix.rows ||
-      colIdx >= matrix.cols) { // Speichergröße nicht überschreiten
-    return 0;
-  }
-
-  MatrixType value = matrix.buffer[rowIdx * matrix.cols + colIdx];
-
-  return value;
-}
-
-Matrix broadCastCols(const Matrix matrix, const unsigned int rows,
-                     const unsigned int cols) {
-
-  Matrix copy = createMatrix(
-      rows, cols); // Matrix 1 Kopie erstellen mit Dimensionen von Matrix2
-  for (int r = 0; r < rows; r++) {
-
-    MatrixType value = getMatrixAt(matrix, r, 0);
-    for (int c = 0; c < cols; c++) {
-
-      setMatrixAt(value, copy, r, c);
-    }
-  }
-
-  return copy;
-}
-Matrix broadCastRows(const Matrix matrix, const unsigned int rows,
-                     const unsigned int cols) {
-
-  Matrix copy = createMatrix(rows, cols);
-
-  for (int c = 0; c < cols; c++) {
-    MatrixType value = getMatrixAt(matrix, 0, c);
-
-    for (int r = 0; r < rows; r++) {
-
-      setMatrixAt(value, copy, r, c);
-    }
-  }
-
-  return copy;
-}
-
-Matrix add(const Matrix matrix1, const Matrix matrix2) {
-
-  // Broadcasting nur bei Vektor und Matrix, Fehlermeldung bei zwei unpassenden
-  // Matrizen
-
-  const unsigned int rows1 = matrix1.rows;
-  const unsigned int rows2 = matrix2.rows;
-  const unsigned int cols1 = matrix1.cols;
-  const unsigned int cols2 = matrix2.cols;
-
-  const int rowsEqual = ((rows1 == rows2) ? 1 : 0);
-
-  const int colsEqual = ((cols1 == cols2) ? 1 : 0);
-
-  if (rowsEqual && colsEqual) // addieren
-
-  {
-    Matrix result = createMatrix(rows1, cols1); // Speicher reservieren
-
-    for (int i = 0; i < (rows1 * cols1); i++) { // addieren
-
-      result.buffer[i] =
-          (matrix1.buffer[i] +
-           matrix2.buffer[i]); // buffer[i]  ⇔  *(buffer + i) Adresse =
-                               // Startadresse + (i * sizeof(MatrixType))
-    }
-
-    return result; // zurückgeben
-  }
-
-  else if (rowsEqual && !colsEqual) {
-
-    if (cols1 == 1) {
-
-      Matrix result = createMatrix(rows2, cols2);
-
-      Matrix copy1 = broadCastCols(matrix1, rows2, cols2);
-
-      for (int i = 0; i < (rows2 * cols2); i++) { // addieren
-
-        result.buffer[i] =
-            (copy1.buffer[i] +
-             matrix2.buffer[i]); // buffer[i]  ⇔  *(buffer + i) Adresse =
-                                 // Startadresse + (i * sizeof(MatrixType))
-      }
-      return result;
-
-      // add und return
-
-    } else if (cols2 == 1) {
-
-      Matrix result = createMatrix(rows1, cols1);
-
-      Matrix copy2 = broadCastCols(matrix2, rows1, cols1);
-
-      for (int i = 0; i < (rows1 * cols1); i++) { // addieren
-
-        result.buffer[i] =
-            (matrix1.buffer[i] +
-             copy2.buffer[i]); // buffer[i]  ⇔  *(buffer + i) Adresse =
-                               // Startadresse + (i * sizeof(MatrixType))
-      }
-      return result;
-      // add und return
-
-    }
-
-    else {
-
-      printf("Fehlermeldung"); // vielleicht Fehlermeldung ändern zu
-                               // Programmabbruch
-      Matrix error = {0, 0, NULL};
-      return error;
-    }
-
-  }
-
-  else if (!rowsEqual && colsEqual) {
-
-    if (rows1 == 1) {
-
-      Matrix result = createMatrix(rows2, cols2);
-
-      Matrix copy1 = broadCastRows(matrix1, rows2, cols2);
-
-      for (int i = 0; i < (rows2 * cols2); i++) { // addieren
-
-        result.buffer[i] =
-            (copy1.buffer[i] +
-             matrix2.buffer[i]); // buffer[i]  ⇔  *(buffer + i) Adresse =
-                                 // Startadresse + (i * sizeof(MatrixType))
-      }
-      return result;
-
-      // add und return
-
-    } else if (rows2 == 1) {
-
-      Matrix result = createMatrix(rows1, cols1);
-
-      Matrix copy2 = broadCastCols(matrix2, rows1, cols1);
-      // add und return
-
-      for (int i = 0; i < (rows1 * cols1); i++) { // addieren
-
-        result.buffer[i] =
-            (matrix1.buffer[i] +
-             copy2.buffer[i]); // buffer[i]  ⇔  *(buffer + i) Adresse =
-                               // Startadresse + (i * sizeof(MatrixType))
-      }
-      return result;
-
-    }
-
-    else {
-
-      printf("Fehlermeldung"); // vielleicht Fehlermeldung ändern zu
-                               // Programmabbruch
-      Matrix error = {0, 0, NULL};
-      return error;
-    }
-  }
-
-  else {
-    printf(
-        "Fehlermeldung"); // vielleicht Fehlermeldung ändern zu Programmabbruch
-    Matrix error = {0, 0, NULL};
-    return error;
-  }
-}
-
-Matrix multiply(const Matrix matrix1, const Matrix matrix2) {
-  // Spalten1 müssen gleich zeilen2 sein! dann multiplizieren
-  if (matrix1.cols == matrix2.rows) {
-    Matrix multMatrix = createMatrix(matrix1.rows, matrix2.cols);
-    // durch neue matrix iterieren
-    for (int r = 0; r < matrix1.rows; r++) {
-      for (int c = 0; c < matrix2.cols; c++) {
-        MatrixType sum = 0.0;
-        // skalarprodukte berechnen, k damit die ganze zeile mal die ganze
-        // spalte genommen wird quasi
-        for (int k = 0; k < matrix1.cols; k++) {
-          // sum+=
-          // matrix1.buffer[r*matrix1.cols+k]*matrix2.buffer[k*matrix2.cols+c];
-          sum += getMatrixAt(matrix1, r, k) * getMatrixAt(matrix2, k, c);
-        }
-        // Ergebnisse in neue matrix speichern
-        setMatrixAt(sum, multMatrix, r, c);
-      }
-    }
-    return multMatrix;
-  }
-  // sonst fehler, kein multiply möglich
-  else {
-    Matrix errorMatrix = {0, 0, NULL};
-    return errorMatrix;
-  }
-}

neuralN_readFiles

@@ -1,29 +0,0 @@
-Inhalte: Dynamische Speicherverwaltung, Strukturen, Dateien lesen.
-
-Ziel: Die Bilder aus mnist_test.info 2 auslesen
-
-Struktur für einlesen des Strings am Anfang der Datei:
-	int AnzahlBilder
-	int breiteBilder
-	int LaengeBilder
-
-Struktur für Bilder:
-	unsinged int array Breite * Höhe
-	unsigned int Klasse (Label 0 - 9)
-
-
-Speicher für Bilder dynamisch allokieren
-
-GrayScaleImageSeries:
-	datei einlesen
-	header String aus der Datei lesen
-	mit header String den benötigten Speicher freigeben
-	in den Speicher die Datei einschreiben (mit Hilfsfunktion)
-
-Hilfsfunktion (saveFile)
-	gehe zum Anfang des Strings
-	speicher alles der Reihe nach ein
-
-clearSeries:
-	pointer der be malloc kommt nehemen
-	free()

neuralNetwork.c

@@ -1,235 +0,0 @@
-#include "neuralNetwork.h"
-#include <math.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-
-#define BUFFER_SIZE 100
-#define FILE_HEADER_STRING "__info2_neural_network_file_format__"
-
-static void softmax(Matrix *matrix) {
-  if (matrix->cols > 0) {
-    double *colSums = (double *)calloc(matrix->cols, sizeof(double));
-
-    if (colSums != NULL) {
-      for (int colIdx = 0; colIdx < matrix->cols; colIdx++) {
-        for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) {
-          MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx));
-          setMatrixAt(expValue, *matrix, rowIdx, colIdx);
-          colSums[colIdx] += expValue;
-        }
-      }
-
-      for (int colIdx = 0; colIdx < matrix->cols; colIdx++) {
-        for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) {
-          MatrixType normalizedValue =
-              getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx];
-          setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx);
-        }
-      }
-      free(colSums);
-    }
-  }
-}
-
-static void relu(Matrix *matrix) {
-  for (int i = 0; i < matrix->rows * matrix->cols; i++) {
-    matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0;
-  }
-}
-
-static int checkFileHeader(FILE *file) {
-  int isValid = 0;
-  int fileHeaderLen = strlen(FILE_HEADER_STRING);
-  char buffer[BUFFER_SIZE] = {0};
-
-  if (BUFFER_SIZE - 1 < fileHeaderLen)
-    fileHeaderLen = BUFFER_SIZE - 1;
-
-  if (fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen)
-    isValid = strcmp(buffer, FILE_HEADER_STRING) == 0;
-
-  return isValid;
-}
-
-static unsigned int readDimension(FILE *file) {
-  int dimension = 0;
-
-  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)
-      clearMatrix(&matrix);
-  }
-
-  return matrix;
-}
-
-static Layer readLayer(FILE *file, unsigned int inputDimension,
-                       unsigned int outputDimension) {
-  Layer layer;
-  layer.weights = readMatrix(file, outputDimension, inputDimension);
-  layer.biases = readMatrix(file, outputDimension, 1);
-
-  return layer;
-}
-
-static int isEmptyLayer(const Layer layer) {
-  return layer.biases.cols == 0 || layer.biases.rows == 0 ||
-         layer.biases.buffer == NULL || layer.weights.rows == 0 ||
-         layer.weights.cols == 0 || layer.weights.buffer == NULL;
-}
-
-static void clearLayer(Layer *layer) {
-  if (layer != NULL) {
-    clearMatrix(&layer->weights);
-    clearMatrix(&layer->biases);
-    layer->activation = NULL;
-  }
-}
-
-static void assignActivations(NeuralNetwork model) {
-  for (int i = 0; i < (int)model.numberOfLayers - 1; i++) {
-    model.layers[i].activation = relu;
-  }
-
-  if (model.numberOfLayers > 0)
-    model.layers[model.numberOfLayers - 1].activation = softmax;
-}
-
-NeuralNetwork loadModel(const char *path) {
-  NeuralNetwork model = {NULL, 0};
-  FILE *file = fopen(path, "rb");
-
-  if (file != NULL) {
-    if (checkFileHeader(file)) {
-      unsigned int inputDimension = readDimension(file);
-      unsigned int outputDimension = readDimension(file);
-
-      while (inputDimension > 0 && outputDimension > 0) {
-        Layer layer = readLayer(file, inputDimension, outputDimension);
-        Layer *layerBuffer = NULL;
-
-        if (isEmptyLayer(layer)) {
-          clearLayer(&layer);
-          clearModel(&model);
-          break;
-        }
-
-        layerBuffer = (Layer *)realloc(
-            model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
-
-        if (layerBuffer != NULL)
-          model.layers = layerBuffer;
-        else {
-          clearModel(&model);
-          break;
-        }
-
-        model.layers[model.numberOfLayers] = layer;
-        model.numberOfLayers++;
-
-        inputDimension = outputDimension;
-        outputDimension = readDimension(file);
-      }
-    }
-    fclose(file);
-
-    assignActivations(model);
-  }
-
-  return model;
-}
-
-static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[],
-                                               unsigned int count) {
-  Matrix matrix = {0, 0, NULL}; // falsch herum
-
-  if (count > 0 && images != NULL) {
-    matrix = createMatrix(images[0].height * images[0].width, count);
-
-    if (matrix.buffer != NULL) {
-      for (int i = 0; i < count; i++) {
-        for (int j = 0; j < images[i].width * images[i].height; j++) {
-          setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i);
-        }
-      }
-    }
-  }
-
-  return matrix;
-}
-
-static Matrix forward(const NeuralNetwork model, Matrix inputBatch) {
-  Matrix result = inputBatch;
-
-  if (result.buffer != NULL) {
-    for (int i = 0; i < model.numberOfLayers; i++) {
-      Matrix biasResult;
-      Matrix weightResult;
-
-      weightResult = multiply(model.layers[i].weights, result);
-      clearMatrix(&result);
-      biasResult = add(model.layers[i].biases, weightResult);
-      clearMatrix(&weightResult);
-
-      if (model.layers[i].activation != NULL)
-        model.layers[i].activation(&biasResult);
-      result = biasResult;
-    }
-  }
-
-  return result;
-}
-
-unsigned char *argmax(const Matrix matrix) {
-  unsigned char *maxIdx = NULL;
-
-  if (matrix.rows > 0 && matrix.cols > 0) {
-    maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols);
-
-    if (maxIdx != NULL) {
-      for (int colIdx = 0; colIdx < matrix.cols; colIdx++) {
-        maxIdx[colIdx] = 0;
-
-        for (int rowIdx = 1; rowIdx < matrix.rows; rowIdx++) {
-          if (getMatrixAt(matrix, rowIdx, colIdx) >
-              getMatrixAt(matrix, maxIdx[colIdx], colIdx))
-            maxIdx[colIdx] = rowIdx;
-        }
-      }
-    }
-  }
-
-  return maxIdx;
-}
-
-unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
-                       unsigned int numberOfImages) {
-  Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages);
-  Matrix outputBatch = forward(model, inputBatch);
-
-  unsigned char *result = argmax(outputBatch);
-
-  clearMatrix(&outputBatch);
-
-  return result;
-}
-
-void clearModel(NeuralNetwork *model) {
-  if (model != NULL) {
-    for (int i = 0; i < model->numberOfLayers; i++) {
-      clearLayer(&model->layers[i]);
-    }
-    model->layers = NULL;
-    model->numberOfLayers = 0;
-  }
-}

neuralNetworkTests.c

@@ -1,308 +0,0 @@
-#include "neuralNetwork.h"
-#include "unity.h"
-#include <math.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-
-static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) {
-  FILE *f = fopen(path, "wb");
-  if (f == NULL)
-    return;
-
-  /* 1) Header: exakt das String, ohne '\n' oder abschließendes '\0' */
-  const char header[] = "__info2_neural_network_file_format__";
-  fwrite(header, sizeof(char), strlen(header), f);
-
-  /* Wenn es keine Layer gibt, kein Dimensionspaar schreiben (loadModel
-     wird beim Lesen dann 0 zurückgeben). Aber wir können auch frühzeitig
-     mit einem 0-Int terminieren — beides ist in Ordnung. */
-  if (nn.numberOfLayers == 0) {
-    /* optional: schreibe ein 0 als next outputDimension (nicht nötig) */
-    int zero = 0;
-    fwrite(&zero, sizeof(int), 1, f);
-    fclose(f);
-    return;
-  }
-
-  /* 2) Für die erste Layer schreiben wir inputDimension und outputDimension */
-  /* inputDimension == weights.cols, outputDimension == weights.rows */
-  int inputDim = (int)nn.layers[0].weights.cols;
-  int outputDim = (int)nn.layers[0].weights.rows;
-  fwrite(&inputDim, sizeof(int), 1, f);
-  fwrite(&outputDim, sizeof(int), 1, f);
-
-  /* 3) Für jede Layer in Reihenfolge: Gewichte (output x input), Biases (output
-     x 1). Zwischen Layern wird nur die nächste outputDimension (int)
-     geschrieben. */
-  for (int i = 0; i < nn.numberOfLayers; i++) {
-    Layer layer = nn.layers[i];
-
-    int wrows = (int)layer.weights.rows;
-    int wcols = (int)layer.weights.cols;
-    int wcount = wrows * wcols;
-    int bcount =
-        layer.biases.rows * layer.biases.cols; /* normalerweise rows * 1 */
-
-    /* Gewichte (MatrixType binär) */
-    if (wcount > 0 && layer.weights.buffer != NULL) {
-      fwrite(layer.weights.buffer, sizeof(MatrixType), (size_t)wcount, f);
-    }
-
-    /* Biases (MatrixType binär) */
-    if (bcount > 0 && layer.biases.buffer != NULL) {
-      fwrite(layer.biases.buffer, sizeof(MatrixType), (size_t)bcount, f);
-    }
-
-    /* Für die nächste Layer: falls vorhanden, schreibe deren outputDimension */
-    if (i + 1 < nn.numberOfLayers) {
-      int nextOutput = (int)nn.layers[i + 1].weights.rows;
-      fwrite(&nextOutput, sizeof(int), 1, f);
-    } else {
-      /* Letzte Layer: wir können das Ende signalisieren, indem wir ein 0
-         schreiben. loadModel liest dann outputDimension = 0 und beendet die
-         Schleife. */
-      int zero = 0;
-      fwrite(&zero, sizeof(int), 1, f);
-    }
-  }
-
-  fclose(f);
-}
-
-void test_loadModelReturnsCorrectNumberOfLayers(void) {
-  const char *path = "some__nn_test_file.info2";
-  MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
-  MatrixType buffer2[] = {1, 2, 3, 4, 5, 6};
-  Matrix weights1 = {.buffer = buffer1, .rows = 3, .cols = 2};
-  Matrix weights2 = {.buffer = buffer2, .rows = 2, .cols = 3};
-  MatrixType buffer3[] = {1, 2, 3};
-  MatrixType buffer4[] = {1, 2};
-  Matrix biases1 = {.buffer = buffer3, .rows = 3, .cols = 1};
-  Matrix biases2 = {.buffer = buffer4, .rows = 2, .cols = 1};
-  Layer layers[] = {{.weights = weights1, .biases = biases1},
-                    {.weights = weights2, .biases = biases2}};
-
-  NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 2};
-  NeuralNetwork netUnderTest;
-
-  prepareNeuralNetworkFile(path, expectedNet);
-
-  netUnderTest = loadModel(path);
-  remove(path);
-
-  TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers,
-                        netUnderTest.numberOfLayers);
-  clearModel(&netUnderTest);
-}
-
-void test_loadModelReturnsCorrectWeightDimensions(void) {
-  const char *path = "some__nn_test_file.info2";
-  MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-  Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
-  MatrixType biasBuffer[] = {7, 8, 9};
-  Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
-  Layer layers[] = {{.weights = weights, .biases = biases}};
-
-  NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
-  NeuralNetwork netUnderTest;
-
-  prepareNeuralNetworkFile(path, expectedNet);
-
-  netUnderTest = loadModel(path);
-  remove(path);
-
-  TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
-  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows,
-                        netUnderTest.layers[0].weights.rows);
-  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols,
-                        netUnderTest.layers[0].weights.cols);
-  clearModel(&netUnderTest);
-}
-
-void test_loadModelReturnsCorrectBiasDimensions(void) {
-  const char *path = "some__nn_test_file.info2";
-  MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-  Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
-  MatrixType biasBuffer[] = {7, 8, 9};
-  Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
-  Layer layers[] = {{.weights = weights, .biases = biases}};
-
-  NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
-  NeuralNetwork netUnderTest;
-
-  prepareNeuralNetworkFile(path, expectedNet);
-
-  netUnderTest = loadModel(path);
-  remove(path);
-
-  TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
-  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.rows,
-                        netUnderTest.layers[0].biases.rows);
-  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.cols,
-                        netUnderTest.layers[0].biases.cols);
-  clearModel(&netUnderTest);
-}
-
-void test_loadModelReturnsCorrectWeights(void) {
-  const char *path = "some__nn_test_file.info2";
-  MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-  Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
-  MatrixType biasBuffer[] = {7, 8, 9};
-  Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
-  Layer layers[] = {{.weights = weights, .biases = biases}};
-
-  NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
-  NeuralNetwork netUnderTest;
-
-  prepareNeuralNetworkFile(path, expectedNet);
-
-  netUnderTest = loadModel(path);
-  remove(path);
-
-  TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
-  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows,
-                        netUnderTest.layers[0].weights.rows);
-  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols,
-                        netUnderTest.layers[0].weights.cols);
-  int n =
-      netUnderTest.layers[0].weights.rows * netUnderTest.layers[0].weights.cols;
-  TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].weights.buffer,
-                              netUnderTest.layers[0].weights.buffer, n);
-  clearModel(&netUnderTest);
-}
-
-void test_loadModelReturnsCorrectBiases(void) {
-  const char *path = "some__nn_test_file.info2";
-  MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-  Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
-  MatrixType biasBuffer[] = {7, 8, 9};
-  Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
-  Layer layers[] = {{.weights = weights, .biases = biases}};
-
-  NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
-  NeuralNetwork netUnderTest;
-
-  prepareNeuralNetworkFile(path, expectedNet);
-
-  netUnderTest = loadModel(path);
-  remove(path);
-
-  TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
-  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows,
-                        netUnderTest.layers[0].weights.rows);
-  TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols,
-                        netUnderTest.layers[0].weights.cols);
-  int n =
-      netUnderTest.layers[0].biases.rows * netUnderTest.layers[0].biases.cols;
-  TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].biases.buffer,
-                              netUnderTest.layers[0].biases.buffer, n);
-  clearModel(&netUnderTest);
-}
-
-void test_loadModelFailsOnWrongFileTag(void) {
-  const char *path = "some_nn_test_file.info2";
-  NeuralNetwork netUnderTest;
-  FILE *file = fopen(path, "wb");
-
-  if (file != NULL) {
-    const char *fileTag = "info2_neural_network_file_format";
-
-    fwrite(fileTag, sizeof(char), strlen(fileTag), file);
-
-    fclose(file);
-  }
-
-  netUnderTest = loadModel(path);
-
-  remove(path);
-
-  TEST_ASSERT_NULL(netUnderTest.layers);
-  TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
-}
-
-void test_clearModelSetsMembersToNull(void) {
-  const char *path = "some__nn_test_file.info2";
-  MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
-  Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
-  MatrixType biasBuffer[] = {7, 8, 9};
-  Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
-  Layer layers[] = {{.weights = weights, .biases = biases}};
-
-  NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
-  NeuralNetwork netUnderTest;
-
-  prepareNeuralNetworkFile(path, expectedNet);
-
-  netUnderTest = loadModel(path);
-  remove(path);
-
-  TEST_ASSERT_NOT_NULL(netUnderTest.layers);
-  TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
-  clearModel(&netUnderTest);
-  TEST_ASSERT_NULL(netUnderTest.layers);
-  TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
-}
-
-static void someActivation(Matrix *matrix) {
-  for (int i = 0; i < matrix->rows * matrix->cols; i++) {
-    matrix->buffer[i] = fabs(matrix->buffer[i]);
-  }
-}
-
-void test_predictReturnsCorrectLabels(void) {
-  const unsigned char expectedLabels[] = {4, 2};
-  GrayScalePixelType imageBuffer1[] = {10, 30, 25, 17};
-  GrayScalePixelType imageBuffer2[] = {20, 40, 10, 128};
-  GrayScaleImage inputImages[] = {
-      {.buffer = imageBuffer1, .width = 2, .height = 2},
-      {.buffer = imageBuffer2, .width = 2, .height = 2}};
-  MatrixType weightsBuffer1[] = {1, -2, 3, -4, 5, -6, 7, -8};
-  MatrixType weightsBuffer2[] = {-9, 10, 11, 12, 13, 14};
-  MatrixType weightsBuffer3[] = {-15, 16,  17, 18, -19, 20,  21, 22,
-                                 23,  -24, 25, 26, 27,  -28, -29};
-  Matrix weights1 = {.buffer = weightsBuffer1, .rows = 2, .cols = 4};
-  Matrix weights2 = {.buffer = weightsBuffer2, .rows = 3, .cols = 2};
-  Matrix weights3 = {.buffer = weightsBuffer3, .rows = 5, .cols = 3};
-  MatrixType biasBuffer1[] = {200, 0};
-  MatrixType biasBuffer2[] = {0, -100, 0};
-  MatrixType biasBuffer3[] = {0, -1000, 0, 2000, 0};
-  Matrix biases1 = {.buffer = biasBuffer1, .rows = 2, .cols = 1};
-  Matrix biases2 = {.buffer = biasBuffer2, .rows = 3, .cols = 1};
-  Matrix biases3 = {.buffer = biasBuffer3, .rows = 5, .cols = 1};
-  Layer layers[] = {
-      {.weights = weights1, .biases = biases1, .activation = someActivation},
-      {.weights = weights2, .biases = biases2, .activation = someActivation},
-      {.weights = weights3, .biases = biases3, .activation = someActivation}};
-  NeuralNetwork netUnderTest = {.layers = layers, .numberOfLayers = 3};
-  unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2);
-  TEST_ASSERT_NOT_NULL(predictedLabels);
-  int n = (int)(sizeof(expectedLabels) / sizeof(expectedLabels[0]));
-  TEST_ASSERT_EQUAL_UINT8_ARRAY(expectedLabels, predictedLabels, n);
-  free(predictedLabels);
-}
-
-void setUp(void) {
-  // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
-}
-
-void tearDown(void) {
-  // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
-}
-
-int main() {
-  UNITY_BEGIN();
-
-  printf("\n============================\nNeural network "
-         "tests\n============================\n");
-  RUN_TEST(test_loadModelReturnsCorrectNumberOfLayers);
-  RUN_TEST(test_loadModelReturnsCorrectWeightDimensions);
-  RUN_TEST(test_loadModelReturnsCorrectBiasDimensions);
-  RUN_TEST(test_loadModelReturnsCorrectWeights);
-  RUN_TEST(test_loadModelReturnsCorrectBiases);
-  RUN_TEST(test_loadModelFailsOnWrongFileTag);
-  RUN_TEST(test_clearModelSetsMembersToNull);
-  RUN_TEST(test_predictReturnsCorrectLabels);
-
-  return UNITY_END();
-}