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info2praktikum-neuronalesnetz/imageInput.c

@@ -6,7 +6,6 @@
 #define BUFFER_SIZE 100
 #define FILE_HEADER_STRING "__info2_image_file_format__"
 
-// TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
 // Lädt eine .info2-Datei und prüft, ob das Dateiformat korrekt ist
 static FILE* openImageFile(const char* path) {
     FILE* imageFile = NULL;
@@ -95,11 +94,9 @@ static char getLabelOfImage(FILE* imageFile, GrayScaleImage* image) {
     return label;
 }
 
-// TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
 GrayScaleImageSeries *readImages(const char *path)
 {
     // Datei laden und Dateiformat prüfen
-    // setzt außerdem den Pointer an der Stelle nach dem Prestring
     FILE* imageFile = openImageFile(path);
 
     if (imageFile == NULL) {
@@ -180,20 +177,36 @@ GrayScaleImageSeries *readImages(const char *path)
         }
     }
 
-    // für jedes Bild gemäß GreyScaleImageSeries.count:
-        // GreyScaleImage.width = width
-        // GreyScaleImage.height = height
-        // GreyScaleImageSeries.buffer = Pixelwerte
-        // labels = Label des Bilds
-        // series.labels[i] = (unsigned char)(i % 256);
-        // Springe in der Datei an die Stelle nach dem i. Bild
-
     fclose(imageFile);
 
     return series;
 }
 
-// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
 void clearSeries(GrayScaleImageSeries *series)
 {
+  //prüfen, ob series überhaupt bereinigt werden muss
+  if(series == NULL){
+    return;
+  }
+
+  //images freigeben und NULL setzen -> jeden Index der Images durchgehen und buffer freigeben
+  if(series->images != NULL){
+    for(unsigned int i = 0; i < series->count; i++){
+      if (series->images[i].buffer != NULL){
+      	free(series->images[i].buffer);
+        series->images[i].buffer = NULL;
+      }
+    }
+    free(series->images); // wenn alle images bereinigt sind, den Zeiger Images selbst bereinigen
+    series->images = NULL;
+  }
+
+  //labels freigeben und NULL setzen
+  if(series->labels != NULL){
+    free(series->labels);
+    series->labels = NULL;
+  }
+
+  //series freigeben
+  free(series);
 }

info2praktikum-neuronalesnetz/imageInputTests.c

@@ -4,6 +4,7 @@
 #include <string.h>
 #include "unity.h"
 #include "imageInput.h"
+#define FILE_HEADER_STRING "__info2_image_file_format__"
 
 
 static void prepareImageFile(const char *path, unsigned short int width, unsigned short int height, unsigned int short numberOfImages, unsigned char label)
@@ -64,6 +65,19 @@ static void prepareImageFileIncorrectTag(const char *path, unsigned short int wi
     }
 }
 
+void test_readImagesFailsOnIncompleteHeader(void) {
+    const char *path = "testIncompleteHeader.info2";
+    FILE *file = fopen(path, "wb");
+    if (file) {
+        fwrite(FILE_HEADER_STRING, 1, strlen(FILE_HEADER_STRING), file);
+
+        unsigned char incompleteHeader[2] = {0x01, 0x00};
+        fwrite(incompleteHeader, 1, 2, file);
+        fclose(file);
+    }
+    TEST_ASSERT_NULL(readImages(path));
+    remove(path);
+}
 
 void test_readImagesReturnsCorrectNumberOfImages(void)
 {
@@ -159,6 +173,37 @@ void test_openImageFileIncorrectTag(void)
     remove(path);
 }
 
+void test_clearSeriesFreesMemory(void)
+{
+  	// Dummy-Series erstellen
+    GrayScaleImageSeries *series = (GrayScaleImageSeries*)malloc(sizeof(GrayScaleImageSeries));
+    series->count = 2;
+
+	// Labels allokieren
+    series->labels = (unsigned char*)malloc(series->count * sizeof(unsigned char));
+    for (unsigned int i = 0; i < series->count; i++) {
+        series->labels[i] = (unsigned char)(i + 1);
+    }
+
+    // Images allokieren
+    series->images = (GrayScaleImage*)malloc(series->count * sizeof(GrayScaleImage));
+    for (unsigned int i = 0; i < series->count; i++) {
+        series->images[i].width = 4;
+        series->images[i].height = 4;
+        series->images[i].buffer = (GrayScalePixelType*)malloc(series->images[i].width * series->images[i].height);
+
+        for (unsigned int j = 0; j < series->images[i].width * series->images[i].height; j++){
+          series->images[i].buffer[j] = 0;
+        }
+    }
+
+    // clearSeries aufrufen
+    clearSeries(series);
+
+    // Test
+    TEST_PASS();
+}
+
 void setUp(void) {
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
@@ -179,6 +224,8 @@ int main()
     RUN_TEST(test_readImagesReturnsNullOnNotExistingPath);
     RUN_TEST(test_readImagesFailsOnWrongFileTag);
     RUN_TEST(test_openImageFileIncorrectTag);
+    RUN_TEST(test_readImagesFailsOnIncompleteHeader);
+    RUN_TEST(test_clearSeriesFreesMemory);
 
     return UNITY_END();
 }

info2praktikum-neuronalesnetz/matrix.c

@@ -10,12 +10,12 @@ Matrix createMatrix(unsigned int rows, unsigned int cols)
   Matrix matrix;
   matrix.rows = rows;
   matrix.cols = cols;
-  if(matrix.rows == 0 || matrix.cols == 0){
+  if(matrix.rows == 0 || matrix.cols == 0){ // prüfen, ob ungültige Dimensionen übergeben wurden, wenn ja, "error matrix" erstellen
     matrix.rows = 0;
     matrix.cols = 0;
     matrix.buffer = NULL;
     }
-  else {
+  else { // für gültige Dimensionen: Speicher allokieren
   	matrix.buffer = (float *)malloc(rows * cols * sizeof(MatrixType));
   }
 
@@ -36,50 +36,60 @@ void clearMatrix(Matrix *matrix)
 
 void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-    if(rowIdx >= matrix.rows || colIdx >= matrix.cols){
+    if(rowIdx >= matrix.rows || colIdx >= matrix.cols){ // Gültigkeit der Dimensionen prüfen
       return;
     }
-    matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
+    matrix.buffer[rowIdx * matrix.cols + colIdx] = value; // value an vorgebenener Stelle setzen
 }
 
 MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
-    if(rowIdx >= matrix.rows || colIdx >= matrix.cols){
+    if(rowIdx >= matrix.rows || colIdx >= matrix.cols){ // Gültigkeit der Dimensionen prüfen
       return 0;
       }
     MatrixType value;
-    value = matrix.buffer[rowIdx * matrix.cols + colIdx];
+    value = matrix.buffer[rowIdx * matrix.cols + colIdx]; // value an vorgegebener Stelle auslesen
     return value;
 }
 
 Matrix add(const Matrix matrix1, const Matrix matrix2)
 {
-    if (matrix1.rows != matrix2.rows || matrix1.cols != matrix2.cols) { // Matrixen können nur addiert werden, sofern sie jeweils die gleiche Anzahl Spalten und Zeilen haben
+    size_t rows = (matrix1.rows > matrix2.rows) ? matrix1.rows : matrix2.rows;
+    size_t cols = (matrix1.cols > matrix2.cols) ? matrix1.cols : matrix2.cols;
+
+    // Prüfen, ob Broadcasting möglich ist
+    if (!((matrix1.rows == rows || matrix1.rows == 1) &&
+          (matrix2.rows == rows || matrix2.rows == 1) &&
+          (matrix1.cols == cols || matrix1.cols == 1) &&
+          (matrix2.cols == cols || matrix2.cols == 1))) {
         Matrix errorMatrix = createMatrix(0, 0);
-      errorMatrix.buffer = NULL;
         return errorMatrix;
     }
 
-    Matrix result = createMatrix(matrix1.rows, matrix1.cols);
-    for (size_t i = 0; i < matrix1.rows; i++) {
-      for (size_t j = 0; j < matrix1.cols; j++) {
-        MatrixType sum = getMatrixAt(matrix1, i, j) + getMatrixAt(matrix2, i, j);
-        setMatrixAt(sum, result, i, j);
+    Matrix result = createMatrix(rows, cols);
+
+    for (size_t i = 0; i < rows; i++) {
+        for (size_t j = 0; j < cols; j++) {
+            MatrixType val1 = matrix1.buffer[(i % matrix1.rows) * matrix1.cols + (j % matrix1.cols)];
+            MatrixType val2 = matrix2.buffer[(i % matrix2.rows) * matrix2.cols + (j % matrix2.cols)];
+            result.buffer[i * cols + j] = val1 + val2;
         }
     }
+
     return result;
-  }
+}
 
 
 Matrix multiply(const Matrix matrix1, const Matrix matrix2)
 {
-    if (matrix1.cols != matrix2.rows){
+    if (matrix1.cols != matrix2.rows){ // prüfen, ob Matrizen multipliziert werden dürfen
       Matrix errorMatrix = createMatrix(0, 0);
       errorMatrix.buffer = NULL;
       return errorMatrix;
       }
-    Matrix matrix3 = createMatrix(matrix1.rows, matrix2.cols);
+    Matrix matrix3 = createMatrix(matrix1.rows, matrix2.cols); // Ergebnis-Matrix erstellen
 
+    // Algorithmus für Multiplikation
     for( size_t i = 0; i < matrix1.rows; i++){
       for(size_t j = 0; j < matrix2.cols; j++){
         MatrixType sum = 0;

info2praktikum-neuronalesnetz/matrixTests.c

@@ -71,6 +71,32 @@ void test_addFailsOnDifferentInputDimensions(void)
     TEST_ASSERT_EQUAL_UINT32(0, result.cols);
 }
 
+void test_addSupportsBroadcasting(void)
+{
+    MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
+    MatrixType buffer2[] = {7, 8};
+    Matrix matrix1 = {.rows=2, .cols=3, .buffer=buffer1};
+    Matrix matrix2 = {.rows=2, .cols=1, .buffer=buffer2};
+
+    Matrix result1 = add(matrix1, matrix2);
+    Matrix result2 = add(matrix2, matrix1);
+
+    float expectedResults[] = {8, 9, 10, 12, 13, 14};
+
+    TEST_ASSERT_EQUAL_UINT32(matrix1.rows, result1.rows);
+    TEST_ASSERT_EQUAL_UINT32(matrix1.cols, result1.cols);
+    TEST_ASSERT_EQUAL_UINT32(matrix1.rows, result2.rows);
+    TEST_ASSERT_EQUAL_UINT32(matrix1.cols, result2.cols);
+
+    TEST_ASSERT_EQUAL_INT(sizeof(expectedResults)/sizeof(expectedResults[0]), result1.rows * result1.cols);
+    TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, result1.buffer, result1.cols * result1.rows);
+    TEST_ASSERT_EQUAL_INT(sizeof(expectedResults)/sizeof(expectedResults[0]), result2.rows * result2.cols);
+    TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, result2.buffer, result2.cols * result2.rows);
+
+    free(result1.buffer);
+    free(result2.buffer);
+}
+
 void test_multiplyReturnsCorrectResults(void)
 {
     MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
@@ -159,6 +185,7 @@ int main()
     RUN_TEST(test_clearMatrixSetsMembersToNull);
     RUN_TEST(test_addReturnsCorrectResult);
     RUN_TEST(test_addFailsOnDifferentInputDimensions);
+    RUN_TEST(test_addSupportsBroadcasting);
     RUN_TEST(test_multiplyReturnsCorrectResults);
     RUN_TEST(test_multiplyFailsOnWrongInputDimensions);
     RUN_TEST(test_getMatrixAtReturnsCorrectResult);

info2praktikum-neuronalesnetz/neuralNetwork.c

@@ -118,6 +118,10 @@ static void assignActivations(NeuralNetwork model)
     if(model.numberOfLayers > 0)
         model.layers[model.numberOfLayers-1].activation = softmax;
 }
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include "neuralNetwork.h"
 
 NeuralNetwork loadModel(const char *path)
 {
@@ -168,6 +172,7 @@ NeuralNetwork loadModel(const char *path)
     return model;
 }
 
+
 static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
 {
     Matrix matrix = {NULL, 0, 0};
@@ -254,6 +259,7 @@ unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
     return result;
 }
 
+
 void clearModel(NeuralNetwork *model)
 {
     if(model != NULL)

info2praktikum-neuronalesnetz/neuralNetworkTests.c

@@ -8,7 +8,48 @@
 
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
-    // TODO
+    FILE *file = fopen(path, "wb");
+    if (!file) {
+        perror("Fehler beim Öffnen der Datei");
+        return;
+    }
+
+    // Header schreiben
+    const char *header = "__info2_neural_network_file_format__";
+    fwrite(header, sizeof(char), strlen(header), file);
+
+    // Alle Layer schreiben
+    for (unsigned int i = 0; i < nn.numberOfLayers; i++) {
+        Layer layer = nn.layers[i];
+
+
+        if(i == 0){
+            // beim ersten Layer inputDim und outputDim schreiben
+            unsigned int inputDim = layer.weights.cols;   // Anzahl Eingänge
+            fwrite(&inputDim, sizeof(unsigned int), 1, file);
+            unsigned int outputDim = layer.weights.rows;  // Anzahl Ausgänge
+            fwrite(&outputDim, sizeof(unsigned int), 1, file);
+        }
+        else{
+            // bei allen anderen Layern nur outputDim schreiben
+            unsigned int outputDim = layer.weights.rows;  // Anzahl Ausgänge
+            fwrite(&outputDim, sizeof(unsigned int), 1, file);
+        }
+
+        // Gewichte schreiben
+        size_t weightCount = layer.weights.rows * layer.weights.cols;
+        fwrite(layer.weights.buffer, sizeof(MatrixType), weightCount, file);
+
+        // Biases schreiben
+        size_t biasCount = layer.biases.rows * layer.biases.cols;
+        fwrite(layer.biases.buffer, sizeof(MatrixType), biasCount, file);
+    }
+
+    // End-Marker (outputDim = 0)
+    unsigned int zero = 0;
+    fwrite(&zero, sizeof(unsigned int), 1, file);
+
+    fclose(file);
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)