neuralNetworkTests mit Kommentaren

Reviewed-on: kachelto100370/info2Praktikum-NeuronalesNetz#1

.vscode/settings.json

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

README.md

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

imageInput.c

@@ -7,16 +7,23 @@
 #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)
 {
+
 }

makefile

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

matrix.c

@@ -1,35 +1,245 @@
+#include "matrix.h"
+#include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
-#include "matrix.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 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 clearMatrix(Matrix *matrix)
+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;
 }
 
-void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
+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;
 }
 
-MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
+Matrix add(const Matrix matrix1, const Matrix matrix2) {
-{
+
-    
+  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 add(const Matrix matrix1, const Matrix matrix2)
+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
-Matrix multiply(const Matrix matrix1, const Matrix matrix2)
+    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;
+  }
 }

matrix.h

@@ -6,14 +6,25 @@
 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);
+void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx,
-MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx);
+                 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);
 Matrix add(const Matrix matrix1, const Matrix matrix2);
 Matrix multiply(const Matrix matrix1, const Matrix matrix2);
 
-
 #endif

neuralN_readFiles

@@ -0,0 +1,29 @@
+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

@@ -1,268 +1,235 @@
-#include <stdlib.h>
-#include <stdio.h>
-#include <math.h>
-#include <string.h>
 #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)
+static void softmax(Matrix *matrix) {
-{
+  if (matrix->cols > 0) {
-    if(matrix->cols > 0)
+    double *colSums = (double *)calloc(matrix->cols, sizeof(double));
-    {
-        double *colSums = (double *)calloc(matrix->cols, sizeof(double));
 
-        if(colSums != NULL)
+    if (colSums != NULL) {
-        {
+      for (int colIdx = 0; colIdx < matrix->cols; colIdx++) {
-            for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
+        for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) {
-            {
+          MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx));
-                for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
+          setMatrixAt(expValue, *matrix, rowIdx, colIdx);
-                {
+          colSums[colIdx] += expValue;
-                    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 colIdx = 0; colIdx < matrix->cols; colIdx++) {
-{
+        for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) {
-    for(int i = 0; i < matrix->rows * matrix->cols; i++)
+          MatrixType normalizedValue =
-    {
+              getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx];
-        matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0;
+          setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx);
-    }
-}
-
-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);
+      }
-
+      free(colSums);
-        assignActivations(model);
     }
-
+  }
-    return model;
 }
 
-static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
+static void relu(Matrix *matrix) {
-{
+  for (int i = 0; i < matrix->rows * matrix->cols; i++) {
-    Matrix matrix = {NULL, 0, 0};
+    matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0;
+  }
+}
 
-    if(count > 0 && images != NULL)
+static int checkFileHeader(FILE *file) {
-    {
+  int isValid = 0;
-        matrix = createMatrix(images[0].height * images[0].width, count);
+  int fileHeaderLen = strlen(FILE_HEADER_STRING);
+  char buffer[BUFFER_SIZE] = {0};
 
-        if(matrix.buffer != NULL)
+  if (BUFFER_SIZE - 1 < fileHeaderLen)
-        {
+    fileHeaderLen = BUFFER_SIZE - 1;
-            for(int i = 0; i < count; i++)
+
-            {
+  if (fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen)
-                for(int j = 0; j < images[i].width * images[i].height; j++)
+    isValid = strcmp(buffer, FILE_HEADER_STRING) == 0;
-                {
+
-                    setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i);
+  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;
         }
-    }
 
-    return matrix;
+        layerBuffer = (Layer *)realloc(
-}
+            model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
 
-static Matrix forward(const NeuralNetwork model, Matrix inputBatch)
+        if (layerBuffer != NULL)
-{
+          model.layers = layerBuffer;
-    Matrix result = inputBatch;
+        else {
-
+          clearModel(&model);
-    if(result.buffer != NULL)
+          break;
-    {
-        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;
+        model.layers[model.numberOfLayers] = layer;
+        model.numberOfLayers++;
+
+        inputDimension = outputDimension;
+        outputDimension = readDimension(file);
+      }
+    }
+    fclose(file);
+
+    assignActivations(model);
+  }
+
+  return model;
 }
 
-unsigned char *argmax(const Matrix matrix)
+static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[],
-{
+                                               unsigned int count) {
-    unsigned char *maxIdx = NULL;
+  Matrix matrix = {0, 0, NULL}; // falsch herum
 
-    if(matrix.rows > 0 && matrix.cols > 0)
+  if (count > 0 && images != NULL) {
-    {
+    matrix = createMatrix(images[0].height * images[0].width, count);
-        maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols);
 
-        if(maxIdx != NULL)
+    if (matrix.buffer != NULL) {
-        {
+      for (int i = 0; i < count; i++) {
-            for(int colIdx = 0; colIdx < matrix.cols; colIdx++)
+        for (int j = 0; j < images[i].width * images[i].height; j++) {
-            {
+          setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i);
-                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;
+  return matrix;
 }
 
-unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[], unsigned int numberOfImages)
+static Matrix forward(const NeuralNetwork model, Matrix inputBatch) {
-{
+  Matrix result = inputBatch;
-    Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages);
-    Matrix outputBatch = forward(model, inputBatch);
 
-    unsigned char *result = argmax(outputBatch);
+  if (result.buffer != NULL) {
-    
+    for (int i = 0; i < model.numberOfLayers; i++) {
-    clearMatrix(&outputBatch);
+      Matrix biasResult;
-    
+      Matrix weightResult;
-    return result;
+
+      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;
 }
 
-void clearModel(NeuralNetwork *model)
+unsigned char *argmax(const Matrix matrix) {
-{
+  unsigned char *maxIdx = NULL;
-    if(model != NULL)
+
-    {
+  if (matrix.rows > 0 && matrix.cols > 0) {
-        for(int i = 0; i < model->numberOfLayers; i++)
+    maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols);
-        {
+
-            clearLayer(&model->layers[i]);
+    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;
         }
-        model->layers = NULL;
+      }
-        model->numberOfLayers = 0;
     }
+  }
+
+  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

@@ -1,242 +1,330 @@
+#include "neuralNetwork.h"
+#include "unity.h"
+#include <math.h>
 #include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
-#include <math.h>
-#include "unity.h"
-#include "neuralNetwork.h"
 
-
+/*typedef struct
-static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    Matrix weights;
-}
+    Matrix biases;
+    ActivationFunctionType activation;
+} Layer;
 
-void test_loadModelReturnsCorrectNumberOfLayers(void)
+typedef struct
 {
-    const char *path = "some__nn_test_file.info2";
+    Layer *layers;
-    MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
+    unsigned int numberOfLayers;
-    MatrixType buffer2[] = {1, 2, 3, 4, 5, 6};
+} NeuralNetwork;*/
-    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};
+/*Layer: Ebene im neuronalen Netzwerk, besteht aus mehreren Neuronen
-    NeuralNetwork netUnderTest;
+Input-Layer: Eingabedatei
+Hidden-Layer: verarbeiten die Daten
+Output-Layer: Ergebnis
 
-    prepareNeuralNetworkFile(path, expectedNet);
 
-    netUnderTest = loadModel(path);
+Gewichte: bestimmen, wie stark ein Eingangssignal auf ein Neuron wirkt
-    remove(path);
 
-    TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers, netUnderTest.numberOfLayers);
+Dimension: Form der Matrizen für einen Layer*/
-    clearModel(&netUnderTest);
-}
 
-void test_loadModelReturnsCorrectWeightDimensions(void)
+// speichert NeuralNetwork nn in binäre Datei->erzeugt Dateiformat
-{
+static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) {
-    const char *path = "some__nn_test_file.info2";
+  FILE *f = fopen(path, "wb"); // Binärdatei zum Schreiben öffnen
-    MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
+  if (f == NULL)
-    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+    return;
-    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};
+  // Header ist Erkennungsstring am Anfang der Datei, loadmodel erkennt
-    NeuralNetwork netUnderTest;
+  // Dateiformat
+  const char header[] = "__info2_neural_network_file_format__";
+  fwrite(header, sizeof(char), strlen(header), f);
 
-    prepareNeuralNetworkFile(path, expectedNet);
+  // Wenn es keine Layer gibt, 0 eintragen, LoadModel gibt 0 zurück
+  if (nn.numberOfLayers == 0) {
+    int zero = 0;
+    fwrite(&zero, sizeof(int), 1, f);
+    fclose(f);
+    return;
+  }
 
-    netUnderTest = loadModel(path);
+  // Layer 0, inputDimension: Anzahl Input-Neuronen, outputDimension: Anzahl
-    remove(path);
+  // Output-Neuronen
+  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);
 
-    TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
+  /* 3) Für jede Layer in Reihenfolge: Gewichte (output x input), Biases (output
-    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows);
+     x 1). Zwischen Layern wird nur die nächste outputDimension (int)
-    TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols);
+     geschrieben. */
-    clearModel(&netUnderTest);
+  for (int i = 0; i < nn.numberOfLayers; i++) {
-}
+    Layer layer = nn.layers[i];
 
-void test_loadModelReturnsCorrectBiasDimensions(void)
+    int wrows = (int)layer.weights.rows;
-{
+    int wcols = (int)layer.weights.cols;
-    const char *path = "some__nn_test_file.info2";
+    int wcount = wrows * wcols;
-    MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
+    int bcount =
-    Matrix weights = {.buffer=weightBuffer, .rows=3, .cols=2};
+        layer.biases.rows * layer.biases.cols; /* normalerweise rows * 1 */
-    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};
+    /* Gewichte (MatrixType binär) */
-    NeuralNetwork netUnderTest;
+    if (wcount > 0 && layer.weights.buffer != NULL) {
-
+      fwrite(layer.weights.buffer, sizeof(MatrixType), (size_t)wcount, f);
-    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);
+    /* Biases (MatrixType binär) */
-
+    if (bcount > 0 && layer.biases.buffer != NULL) {
-    remove(path);
+      fwrite(layer.biases.buffer, sizeof(MatrixType), (size_t)bcount, f);
-
-    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]);
     }
+
+    /* 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_predictReturnsCorrectLabels(void)
+void test_loadModelReturnsCorrectNumberOfLayers(void) {
-{
+  const char *path = "some__nn_test_file.info2";
-    const unsigned char expectedLabels[] = {4, 2};
+  MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
-    GrayScalePixelType imageBuffer1[] = {10, 30, 25, 17};
+  MatrixType buffer2[] = {1, 2, 3, 4, 5, 6};
-    GrayScalePixelType imageBuffer2[] = {20, 40, 10, 128};
+  Matrix weights1 = {.buffer = buffer1, .rows = 3, .cols = 2};
-    GrayScaleImage inputImages[] = {{.buffer=imageBuffer1, .width=2, .height=2}, {.buffer=imageBuffer2, .width=2, .height=2}};
+  Matrix weights2 = {.buffer = buffer2, .rows = 2, .cols = 3};
-    MatrixType weightsBuffer1[] = {1, -2, 3, -4, 5, -6, 7, -8};
+  MatrixType buffer3[] = {1, 2, 3};
-    MatrixType weightsBuffer2[] = {-9, 10, 11, 12, 13, 14};
+  MatrixType buffer4[] = {1, 2};
-    MatrixType weightsBuffer3[] = {-15, 16, 17, 18, -19, 20, 21, 22, 23, -24, 25, 26, 27, -28, -29};
+  Matrix biases1 = {.buffer = buffer3, .rows = 3, .cols = 1};
-    Matrix weights1 = {.buffer=weightsBuffer1, .rows=2, .cols=4};
+  Matrix biases2 = {.buffer = buffer4, .rows = 2, .cols = 1};
-    Matrix weights2 = {.buffer=weightsBuffer2, .rows=3, .cols=2};
+  Layer layers[] = {{.weights = weights1, .biases = biases1},
-    Matrix weights3 = {.buffer=weightsBuffer3, .rows=5, .cols=3};
+                    {.weights = weights2, .biases = biases2}};
-    MatrixType biasBuffer1[] = {200, 0};
+
-    MatrixType biasBuffer2[] = {0, -100, 0};
+  NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 2};
-    MatrixType biasBuffer3[] = {0, -1000, 0, 2000, 0};
+  NeuralNetwork netUnderTest;
-    Matrix biases1 = {.buffer=biasBuffer1, .rows=2, .cols=1};
+
-    Matrix biases2 = {.buffer=biasBuffer2, .rows=3, .cols=1};
+  prepareNeuralNetworkFile(path, expectedNet);
-    Matrix biases3 = {.buffer=biasBuffer3, .rows=5, .cols=1};
+
-    Layer layers[] = {{.weights=weights1, .biases=biases1, .activation=someActivation}, \
+  netUnderTest = loadModel(path);
-                      {.weights=weights2, .biases=biases2, .activation=someActivation}, \
+  remove(path);
-                      {.weights=weights3, .biases=biases3, .activation=someActivation}};
+
-    NeuralNetwork netUnderTest = {.layers=layers, .numberOfLayers=3};
+  TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers,
-    unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2);
+                        netUnderTest.numberOfLayers);
-    TEST_ASSERT_NOT_NULL(predictedLabels);
+  clearModel(&netUnderTest);
-    int n = (int)(sizeof(expectedLabels) / sizeof(expectedLabels[0]));
+}
-    TEST_ASSERT_EQUAL_UINT8_ARRAY(expectedLabels, predictedLabels, n);
+
-    free(predictedLabels);
+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
+  // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
 
 void tearDown(void) {
-    // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
+  // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
 }
 
-int main()
+int main() {
-{
+  UNITY_BEGIN();
-    UNITY_BEGIN();
 
-    printf("\n============================\nNeural network tests\n============================\n");
+  printf("\n============================\nNeural network "
-    RUN_TEST(test_loadModelReturnsCorrectNumberOfLayers);
+         "tests\n============================\n");
-    RUN_TEST(test_loadModelReturnsCorrectWeightDimensions);
+  RUN_TEST(test_loadModelReturnsCorrectNumberOfLayers);
-    RUN_TEST(test_loadModelReturnsCorrectBiasDimensions);
+  RUN_TEST(test_loadModelReturnsCorrectWeightDimensions);
-    RUN_TEST(test_loadModelReturnsCorrectWeights);
+  RUN_TEST(test_loadModelReturnsCorrectBiasDimensions);
-    RUN_TEST(test_loadModelReturnsCorrectBiases);
+  RUN_TEST(test_loadModelReturnsCorrectWeights);
-    RUN_TEST(test_loadModelFailsOnWrongFileTag);
+  RUN_TEST(test_loadModelReturnsCorrectBiases);
-    RUN_TEST(test_clearModelSetsMembersToNull);
+  RUN_TEST(test_loadModelFailsOnWrongFileTag);
-    RUN_TEST(test_predictReturnsCorrectLabels);
+  RUN_TEST(test_clearModelSetsMembersToNull);
+  RUN_TEST(test_predictReturnsCorrectLabels);
 
-    return UNITY_END();
+  return UNITY_END();
 }