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Kristin 2025-11-25 10:49:32 +01:00
commit f1af6c1e4a
6 changed files with 715 additions and 453 deletions
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141
imageInput.c
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@ -1,29 +1,134 @@
#include "imageInput.h"
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "imageInput.h"
#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
GrayScaleImage readImage()
{
/* ----------------------------------------------------------
1. Header prüfen
---------------------------------------------------------- */
static int readHeader(FILE *file) {
char header[sizeof(FILE_HEADER_STRING)];
if (fread(header, 1, sizeof(FILE_HEADER_STRING) - 1, file) !=
sizeof(FILE_HEADER_STRING) - 1)
return 0;
header[sizeof(FILE_HEADER_STRING) - 1] = '\0';
return strcmp(header, FILE_HEADER_STRING) == 0;
}
// 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;
/* ----------------------------------------------------------
2. Meta-Daten lesen (unsigned short)
---------------------------------------------------------- */
static int readMeta(FILE *file, unsigned short *count, unsigned short *width,
unsigned short *height) {
if (fread(count, sizeof(unsigned short), 1, file) != 1)
return 0;
if (fread(width, sizeof(unsigned short), 1, file) != 1)
return 0;
if (fread(height, sizeof(unsigned short), 1, file) != 1)
return 0;
return 1;
}
// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
void clearSeries(GrayScaleImageSeries *series)
{
/* ----------------------------------------------------------
3. Einzelbild lesen
---------------------------------------------------------- */
static int readSingleImage(FILE *file, GrayScaleImage *img,
unsigned short width, unsigned short height) {
img->width = width;
img->height = height;
}
size_t numPixels = (size_t)width * (size_t)height;
img->buffer = malloc(numPixels);
if (!img->buffer)
return 0;
if (fread(img->buffer, 1, numPixels, file) != numPixels) {
free(img->buffer);
img->buffer = NULL;
return 0;
}
return 1;
}
/* ----------------------------------------------------------
4. Label lesen
---------------------------------------------------------- */
static int readLabel(FILE *file, unsigned char *label) {
return fread(label, 1, 1, file) == 1;
}
/* ----------------------------------------------------------
5. Komplette Bildserie lesen
---------------------------------------------------------- */
GrayScaleImageSeries *readImages(const char *path) {
FILE *file = fopen(path, "rb");
if (!file)
return NULL;
if (!readHeader(file)) {
fclose(file);
return NULL;
}
unsigned short count, width, height;
if (!readMeta(file, &count, &width, &height)) {
fclose(file);
return NULL;
}
// printf("%d, %d, %d", count, width, height);
GrayScaleImageSeries *series = malloc(sizeof(GrayScaleImageSeries));
if (!series) {
fclose(file);
return NULL;
}
series->count = count;
series->images = malloc(count * sizeof(GrayScaleImage));
series->labels = malloc(count * sizeof(unsigned char));
if (!series->images || !series->labels) {
free(series->images);
free(series->labels);
free(series);
fclose(file);
return NULL;
}
for (unsigned int i = 0; i < count; i++) {
if (!readSingleImage(file, &series->images[i], width, height) ||
!readLabel(file, &series->labels[i])) {
// Aufräumen bei Fehler
for (unsigned int j = 0; j < i; j++) {
free(series->images[j].buffer);
}
free(series->images);
free(series->labels);
free(series);
fclose(file);
return NULL;
}
}
fclose(file);
return series;
}
/* ----------------------------------------------------------
6. Speicher komplett freigeben
---------------------------------------------------------- */
void clearSeries(GrayScaleImageSeries *series) {
if (!series)
return;
for (unsigned int i = 0; i < series->count; i++) {
free(series->images[i].buffer);
}
free(series->images);
free(series->labels);
free(series);
}

293
imageInputTests.c
View File

@ -1,143 +1,204 @@
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include "unity.h"
#include "imageInput.h"
#include "unity.h"
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
/* ---------------------------------------------------------
Hilfsfunktion: Testdatei vorbereiten
--------------------------------------------------------- */
static void prepareImageFile(const char *path, unsigned int width,
unsigned int height, unsigned int numberOfImages,
unsigned char label) {
FILE *file = fopen(path, "wb");
if (!file)
return;
static void prepareImageFile(const char *path, unsigned short int width, unsigned short int height, unsigned int short numberOfImages, unsigned char label)
{
FILE *file = fopen(path, "wb");
// Header
const char *fileTag = "__info2_image_file_format__";
fwrite(fileTag, 1, strlen(fileTag), file);
if(file != NULL)
{
const char *fileTag = "__info2_image_file_format__";
GrayScalePixelType *zeroBuffer = (GrayScalePixelType *)calloc(numberOfImages * width * height, sizeof(GrayScalePixelType));
// Meta-Daten als unsigned short
unsigned short n = (unsigned short)numberOfImages;
unsigned short w = (unsigned short)width;
unsigned short h = (unsigned short)height;
fwrite(&n, sizeof(unsigned short), 1, file);
fwrite(&w, sizeof(unsigned short), 1, file);
fwrite(&h, sizeof(unsigned short), 1, file);
if(zeroBuffer != NULL)
{
fwrite(fileTag, sizeof(fileTag[0]), strlen(fileTag), file);
fwrite(&numberOfImages, sizeof(numberOfImages), 1, file);
fwrite(&width, sizeof(width), 1, file);
fwrite(&height, sizeof(height), 1, file);
// Pixelbuffer
GrayScalePixelType *buffer =
calloc(width * height, sizeof(GrayScalePixelType));
if (!buffer) {
fclose(file);
return;
}
for (unsigned int i = 0; i < width * height; i++)
buffer[i] = (GrayScalePixelType)i;
for(int i = 0; i < numberOfImages; i++)
{
fwrite(zeroBuffer, sizeof(GrayScalePixelType), width * height, file);
fwrite(&label, sizeof(unsigned char), 1, file);
}
// Jedes Bild schreiben: Pixel + Label
for (unsigned int img = 0; img < numberOfImages; img++) {
fwrite(buffer, sizeof(GrayScalePixelType), width * height, file);
fwrite(&label, sizeof(unsigned char), 1, file);
}
free(zeroBuffer);
}
fclose(file);
}
free(buffer);
fclose(file);
}
/* ---------------------------------------------------------
Unit Tests
--------------------------------------------------------- */
void test_readImagesReturnsCorrectNumberOfImages(void)
{
GrayScaleImageSeries *series = NULL;
const unsigned short expectedNumberOfImages = 2;
const char *path = "testFile.info2";
prepareImageFile(path, 8, 8, expectedNumberOfImages, 1);
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_EQUAL_UINT16(expectedNumberOfImages, series->count);
clearSeries(series);
remove(path);
void test_readImagesReturnsCorrectNumberOfImages(void) {
GrayScaleImageSeries *series = NULL;
const unsigned int expectedNumberOfImages = 2;
const char *path = "testFile.info2";
prepareImageFile(path, 8, 8, expectedNumberOfImages, 1);
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_EQUAL_UINT(expectedNumberOfImages, series->count);
clearSeries(series);
remove(path);
}
void test_readImagesReturnsCorrectImageWidth(void)
{
GrayScaleImageSeries *series = NULL;
const unsigned short expectedWidth = 10;
const char *path = "testFile.info2";
prepareImageFile(path, expectedWidth, 8, 2, 1);
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_NOT_NULL(series->images);
TEST_ASSERT_EQUAL_UINT16(2, series->count);
TEST_ASSERT_EQUAL_UINT16(expectedWidth, series->images[0].width);
TEST_ASSERT_EQUAL_UINT16(expectedWidth, series->images[1].width);
clearSeries(series);
remove(path);
void test_readImagesReturnsCorrectImageWidth(void) {
GrayScaleImageSeries *series = NULL;
const unsigned int expectedWidth = 10;
const char *path = "testFile.info2";
prepareImageFile(path, expectedWidth, 8, 2, 1);
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_NOT_NULL(series->images);
TEST_ASSERT_EQUAL_UINT(2, series->count);
TEST_ASSERT_EQUAL_UINT(expectedWidth, series->images[0].width);
TEST_ASSERT_EQUAL_UINT(expectedWidth, series->images[1].width);
clearSeries(series);
remove(path);
}
void test_readImagesReturnsCorrectImageHeight(void)
{
GrayScaleImageSeries *series = NULL;
const unsigned short expectedHeight = 10;
const char *path = "testFile.info2";
prepareImageFile(path, 8, expectedHeight, 2, 1);
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_NOT_NULL(series->images);
TEST_ASSERT_EQUAL_UINT16(2, series->count);
TEST_ASSERT_EQUAL_UINT16(expectedHeight, series->images[0].height);
TEST_ASSERT_EQUAL_UINT16(expectedHeight, series->images[1].height);
clearSeries(series);
remove(path);
void test_readImagesReturnsCorrectImageHeight(void) {
GrayScaleImageSeries *series = NULL;
const unsigned int expectedHeight = 10;
const char *path = "testFile.info2";
prepareImageFile(path, 8, expectedHeight, 2, 1);
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_NOT_NULL(series->images);
TEST_ASSERT_EQUAL_UINT(2, series->count);
TEST_ASSERT_EQUAL_UINT(expectedHeight, series->images[0].height);
TEST_ASSERT_EQUAL_UINT(expectedHeight, series->images[1].height);
clearSeries(series);
remove(path);
}
void test_readImagesReturnsCorrectLabels(void)
{
const unsigned char expectedLabel = 15;
void test_readImagesReturnsCorrectLabels(void) {
const unsigned char expectedLabel = 15;
GrayScaleImageSeries *series = NULL;
const char *path = "testFile.info2";
prepareImageFile(path, 8, 8, 2, expectedLabel);
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_NOT_NULL(series->labels);
TEST_ASSERT_EQUAL_UINT16(2, series->count);
for (int i = 0; i < 2; i++) {
TEST_ASSERT_EQUAL_UINT8(expectedLabel, series->labels[i]);
}
clearSeries(series);
remove(path);
GrayScaleImageSeries *series = NULL;
const char *path = "testFile.info2";
prepareImageFile(path, 8, 8, 2, expectedLabel);
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_NOT_NULL(series->labels);
TEST_ASSERT_EQUAL_UINT(2, series->count);
for (int i = 0; i < 2; i++) {
TEST_ASSERT_EQUAL_UINT8(expectedLabel, series->labels[i]);
}
clearSeries(series);
remove(path);
}
void test_readImagesReturnsNullOnNotExistingPath(void)
{
const char *path = "testFile.txt";
remove(path);
void test_readImagesReturnsNullOnNotExistingPath(void) {
const char *path = "testFile.txt";
remove(path);
TEST_ASSERT_NULL(readImages(path));
}
void test_readImagesFailsOnWrongFileTag(void) {
const char *path = "testFile.info2";
FILE *file = fopen(path, "w");
if (file != NULL) {
fprintf(file, "some_tag ");
fclose(file);
TEST_ASSERT_NULL(readImages(path));
}
remove(path);
}
void test_readImagesFailsOnWrongFileTag(void)
{
const char *path = "testFile.info2";
FILE *file = fopen(path, "w");
if(file != NULL)
{
fprintf(file, "some_tag ");
fclose(file);
TEST_ASSERT_NULL(readImages(path));
}
remove(path);
void test_read_GrayScale_Pixel(void) {
GrayScaleImageSeries *series = NULL;
const char *path = "testFile.info2";
prepareImageFile(path, 8, 8, 1, 1);
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_NOT_NULL(series->images);
TEST_ASSERT_EQUAL_UINT(1, series->count);
for (int i = 0; i < (8 * 8); i++) {
TEST_ASSERT_EQUAL_UINT8((GrayScalePixelType)i, series->images[0].buffer[i]);
}
clearSeries(series);
remove(path);
}
void setUp(void) {
// Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
/* ---------------------------------------------------------
Optional: Mehrere Bilder gleichzeitig testen
--------------------------------------------------------- */
void test_readImagesMultipleImagesContent(void) {
GrayScaleImageSeries *series = NULL;
const char *path = "testFile.info2";
const unsigned int numberOfImages = 3;
const unsigned int width = 4;
const unsigned int height = 4;
const unsigned char label = 7;
prepareImageFile(path, width, height, numberOfImages, label);
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_NOT_NULL(series->images);
TEST_ASSERT_NOT_NULL(series->labels);
TEST_ASSERT_EQUAL_UINT(numberOfImages, series->count);
for (unsigned int img = 0; img < numberOfImages; img++) {
for (unsigned int i = 0; i < width * height; i++)
TEST_ASSERT_EQUAL_UINT8((GrayScalePixelType)i,
series->images[img].buffer[i]);
TEST_ASSERT_EQUAL_UINT8(label, series->labels[img]);
}
clearSeries(series);
remove(path);
}
void tearDown(void) {
// Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
/* ---------------------------------------------------------
Setup / Teardown
--------------------------------------------------------- */
void setUp(void) {}
void tearDown(void) {}
/* ---------------------------------------------------------
main()
--------------------------------------------------------- */
int main(void) {
UNITY_BEGIN();
printf("\n============================\nImage input "
"tests\n============================\n");
RUN_TEST(test_readImagesReturnsCorrectNumberOfImages);
RUN_TEST(test_readImagesReturnsCorrectImageWidth);
RUN_TEST(test_readImagesReturnsCorrectImageHeight);
RUN_TEST(test_readImagesReturnsCorrectLabels);
RUN_TEST(test_readImagesReturnsNullOnNotExistingPath);
RUN_TEST(test_readImagesFailsOnWrongFileTag);
RUN_TEST(test_read_GrayScale_Pixel);
RUN_TEST(test_readImagesMultipleImagesContent);
return UNITY_END();
}
int main()
{
UNITY_BEGIN();
printf("\n============================\nImage input tests\n============================\n");
RUN_TEST(test_readImagesReturnsCorrectNumberOfImages);
RUN_TEST(test_readImagesReturnsCorrectImageWidth);
RUN_TEST(test_readImagesReturnsCorrectImageHeight);
RUN_TEST(test_readImagesReturnsCorrectLabels);
RUN_TEST(test_readImagesReturnsNullOnNotExistingPath);
RUN_TEST(test_readImagesFailsOnWrongFileTag);
return UNITY_END();
}

222
matrix.c
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@ -1,14 +1,16 @@
#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) {
if (cols == 0 || rows == 0){
if (cols == 0 || rows == 0) {
Matrix errorMatrix = {0, 0, NULL};
return errorMatrix;
}
@ -19,11 +21,15 @@ Matrix createMatrix(unsigned int rows, unsigned int cols) {
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
if (matrix->buffer != NULL) {
free((*matrix).buffer);
matrix->buffer = NULL;
}
matrix->rows = 0;
matrix->cols = 0;
}
void setMatrixAt(const MatrixType value, Matrix matrix,
const unsigned int rowIdx, // Kopie der Matrix wird übergeben
const unsigned int colIdx) {
@ -32,42 +38,41 @@ void setMatrixAt(const MatrixType value, Matrix matrix,
// Speichergröße nicht überschreiten
return;
}
matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
// rowIdx * matrix.cols -> Beginn der Zeile colIdx ->Spalte
// innerhalb der Zeile
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;
if (rowIdx >= matrix.rows || colIdx >= matrix.cols ||
matrix.buffer == NULL) { // Speichergröße nicht überschreiten
return UNDEFINED_MATRIX_VALUE;
}
MatrixType value = matrix.buffer[rowIdx * matrix.cols + colIdx];
return value;
}
Matrix broadcastingCols(const Matrix matrix, const unsigned int cols){
Matrix copy1 = createMatrix(matrix.rows, cols);
for (int r= 0; r < matrix.rows; r++){
MatrixType valueMatrix1 = getMatrixAt(matrix, r, 0);
for (int c=0; c < cols; c++){
setMatrixAt(valueMatrix1, copy1,r,c);
}
}
return copy1;
Matrix broadcastingCols(const Matrix matrix, const unsigned int cols) {
Matrix copy1 = createMatrix(matrix.rows, cols);
for (int r = 0; r < matrix.rows; r++) {
MatrixType valueMatrix1 = getMatrixAt(matrix, r, 0);
for (int c = 0; c < cols; c++) {
setMatrixAt(valueMatrix1, copy1, r, c);
}
}
return copy1;
}
Matrix broadcastingRows(const Matrix matrix, const unsigned int rows){
Matrix copy1 = createMatrix(rows, matrix.cols);
for (int c= 0; c < matrix.cols; c++){
MatrixType valueMatrix1 = getMatrixAt(matrix, 0, c);
for (int r=0; r < rows; r++){
setMatrixAt(valueMatrix1, copy1,r,c);
}
}
return copy1;
Matrix broadcastingRows(const Matrix matrix, const unsigned int rows) {
Matrix copy1 = createMatrix(rows, matrix.cols);
for (int c = 0; c < matrix.cols; c++) {
MatrixType valueMatrix1 = getMatrixAt(matrix, 0, c);
for (int r = 0; r < rows; r++) {
setMatrixAt(valueMatrix1, copy1, r, c);
}
}
return copy1;
}
Matrix add(const Matrix matrix1, const Matrix matrix2) {
@ -78,128 +83,125 @@ Matrix add(const Matrix matrix1, const Matrix matrix2) {
const int cols2 = matrix2.cols;
const int rows2 = matrix2.rows;
const int rowsEqual = (matrix1.rows == matrix2.rows) ? 1 : 0;
const int colsEqual = (matrix1.cols == matrix2.cols) ? 1 : 0;
const int rowsEqual = (matrix1.rows==matrix2.rows) ? 1: 0;
const int colsEqual = (matrix1.cols==matrix2.cols) ? 1: 0;
// Broadcasting nur bei Vektor und Matrix, Fehlermeldung bei zwei unpassender
// Matrix
if (rowsEqual == 1 && colsEqual == 1){
if (rowsEqual == 1 && colsEqual == 1) {
Matrix result = createMatrix(matrix1.rows, matrix1.cols);
if (result.buffer == NULL){
return (Matrix){0,0,NULL};
if (result.buffer == NULL) {
return (Matrix){0, 0, NULL};
}
for (int i = 0; i< rows1; i++) {
for (int j= 0; j< cols1; j++){
int valueM1= getMatrixAt(matrix1, i, j);
int valueM2= getMatrixAt(matrix2, i, j);
for (int i = 0; i < rows1; i++) {
for (int j = 0; j < cols1; j++) {
int valueM1 = getMatrixAt(matrix1, i, j);
int valueM2 = getMatrixAt(matrix2, i, j);
int sum = valueM1 + valueM2;
setMatrixAt(sum, result, i, j);
}
}
return result;
}
else if (rowsEqual ==1 && (cols1 ==1 || cols2 ==1)){
if (cols1==1){ //broadcasting von vektor 1 zu matrix 1, add
} else if (rowsEqual == 1 && (cols1 == 1 || cols2 == 1)) {
if (cols1 == 1) { // broadcasting von vektor 1 zu matrix 1, add
Matrix newMatrix = broadcastingCols(matrix1, cols2);
//add
// add
Matrix result = createMatrix(newMatrix.rows, newMatrix.cols);
if (result.buffer == NULL){
return (Matrix){0,0,NULL};
if (result.buffer == NULL) {
return (Matrix){0, 0, NULL};
}
for (int i = 0; i< rows1; i++) {
for (int j= 0; j< cols2; j++){
int valueM1= getMatrixAt(newMatrix, i, j);
int valueM2= getMatrixAt(matrix2, i, j);
int sum = valueM1 + valueM2;
setMatrixAt(sum, result, i, j);
for (int i = 0; i < rows1; i++) {
for (int j = 0; j < cols2; j++) {
int valueM1 = getMatrixAt(newMatrix, i, j);
int valueM2 = getMatrixAt(matrix2, i, j);
int sum = valueM1 + valueM2;
setMatrixAt(sum, result, i, j);
}
}
}
return result;
}
else{
return result;
} else {
Matrix newMatrix2 = broadcastingCols(matrix2, cols1);
//add
// add
Matrix result = createMatrix(newMatrix2.rows, newMatrix2.cols);
if (result.buffer == NULL){
return (Matrix){0,0,NULL};
if (result.buffer == NULL) {
return (Matrix){0, 0, NULL};
}
for (int i = 0; i< rows1; i++) {
for (int j= 0; j< cols1; j++){
int valueM1= getMatrixAt(matrix1, i, j);
int valueM2= getMatrixAt(newMatrix2, i, j);
int sum = valueM1 + valueM2;
setMatrixAt(sum, result, i, j);
for (int i = 0; i < rows1; i++) {
for (int j = 0; j < cols1; j++) {
int valueM1 = getMatrixAt(matrix1, i, j);
int valueM2 = getMatrixAt(newMatrix2, i, j);
int sum = valueM1 + valueM2;
setMatrixAt(sum, result, i, j);
}
}
}
return result;
return result;
}
}
else if ((rows1 ==1 || rows2 ==1) && colsEqual == 1){
if (rows1==1){
else if ((rows1 == 1 || rows2 == 1) && colsEqual == 1) {
if (rows1 == 1) {
Matrix newMatrix = broadcastingRows(matrix1, rows2);
//add
// add
Matrix result = createMatrix(newMatrix.rows, newMatrix.cols);
if (result.buffer == NULL){
return (Matrix){0,0,NULL};
if (result.buffer == NULL) {
return (Matrix){0, 0, NULL};
}
for (int i = 0; i< rows2; i++) {
for (int j= 0; j< cols1; j++){
int valueM1= getMatrixAt(newMatrix, i, j);
int valueM2= getMatrixAt(matrix2, i, j);
int sum = valueM1 + valueM2;
setMatrixAt(sum, result, i, j);
for (int i = 0; i < rows2; i++) {
for (int j = 0; j < cols1; j++) {
int valueM1 = getMatrixAt(newMatrix, i, j);
int valueM2 = getMatrixAt(matrix2, i, j);
int sum = valueM1 + valueM2;
setMatrixAt(sum, result, i, j);
}
}
}
return result;
}
else{
return result;
} else {
Matrix newMatrix2 = broadcastingRows(matrix2, rows1);
//add
// add
Matrix result = createMatrix(newMatrix2.rows, newMatrix2.cols);
if (result.buffer == NULL){
return (Matrix){0,0,NULL};
if (result.buffer == NULL) {
return (Matrix){0, 0, NULL};
}
for (int i = 0; i< rows1; i++) {
for (int j= 0; j< cols1; j++){
int valueM1= getMatrixAt(matrix1, i, j);
int valueM2= getMatrixAt(newMatrix2, i, j);
int sum = valueM1 + valueM2;
setMatrixAt(sum, result, i, j);
for (int i = 0; i < rows1; i++) {
for (int j = 0; j < cols1; j++) {
int valueM1 = getMatrixAt(matrix1, i, j);
int valueM2 = getMatrixAt(newMatrix2, i, j);
int sum = valueM1 + valueM2;
setMatrixAt(sum, result, i, j);
}
}
return result;
}
return result;
}
}
else {
} else {
// kein add möglich
Matrix errorMatrix = {0, 0, NULL};
return errorMatrix;
}
return result;
}
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++){
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);
// 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
// Ergebnisse in neue matrix speichern
setMatrixAt(sum, multMatrix, r, c);
}
}
return multMatrix;
}
//sonst fehler, kein multiply möglich
else{
// sonst fehler, kein multiply möglich
else {
Matrix errorMatrix = {0, 0, NULL};
return errorMatrix;
}

5
matrix.h
View File

@ -19,6 +19,11 @@ 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);
Matrix add(const Matrix matrix1, const Matrix matrix2);
Matrix multiply(const Matrix matrix1, const Matrix matrix2);

2
neuralNetwork.c
View File

@ -151,7 +151,7 @@ NeuralNetwork loadModel(const char *path) {
static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[],
unsigned int count) {
Matrix matrix = {0, 0, NULL};
Matrix matrix = {0, 0, NULL}; // falsch herum
if (count > 0 && images != NULL) {
matrix = createMatrix(images[0].height * images[0].width, count);

505
neuralNetworkTests.c
View File

@ -1,242 +1,331 @@
#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"
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
/*typedef struct
{
// TODO
}
Matrix weights;
Matrix biases;
ActivationFunctionType activation;
} Layer;
void test_loadModelReturnsCorrectNumberOfLayers(void)
typedef struct
{
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}};
Layer *layers;
unsigned int numberOfLayers;
} NeuralNetwork;*/
NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=2};
NeuralNetwork netUnderTest;
/*Layer: Ebene im neuronalen Netzwerk, besteht aus mehreren Neuronen
Input-Layer: Eingabedatei
Hidden-Layer: verarbeiten die Daten
Output-Layer: Ergebnis
prepareNeuralNetworkFile(path, expectedNet);
netUnderTest = loadModel(path);
remove(path);
Gewichte: bestimmen, wie stark ein Eingangssignal auf ein Neuron wirkt
TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers, netUnderTest.numberOfLayers);
clearModel(&netUnderTest);
}
Dimension: Form der Matrizen für einen Layer*/
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}};
// speichert NeuralNetwork nn in binäre Datei->erzeugt Dateiformat
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) {
FILE *fptr = fopen(path, "wb"); // Binärdatei zum Schreiben öffnen
if (fptr == NULL)
return; // file konnte nicht geöffnet werden
NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
NeuralNetwork netUnderTest;
// Header ist Erkennungsstring am Anfang der Datei, loadmodel erkennt
// Dateiformat
const char header[] =
"__info2_neural_network_file_format__"; // header vor jedem Layer
fwrite(header, sizeof(char), strlen(header), fptr);
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, fptr);
fclose(fptr);
return;
}
netUnderTest = loadModel(path);
remove(path);
// Layer 0, inputDimension: Anzahl Input-Neuronen, outputDimension: Anzahl
// Output-Neuronen
int inputDim = (int)nn.layers[0].weights.cols;
int outputDim = (int)nn.layers[0].weights.rows;
fwrite(&inputDim, sizeof(int), 1, fptr);
fwrite(&outputDim, sizeof(int), 1, fptr);
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);
}
/* 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];
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}};
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 */
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);
/* Gewichte (MatrixType binär) */
if (wcount > 0 && layer.weights.buffer != NULL) {
fwrite(layer.weights.buffer, sizeof(MatrixType), (size_t)wcount, fptr);
}
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]);
/* Biases (MatrixType binär) */
if (bcount > 0 && layer.biases.buffer != NULL) {
fwrite(layer.biases.buffer, sizeof(MatrixType), (size_t)bcount, fptr);
}
/* 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, fptr);
} 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, fptr);
}
}
fclose(fptr);
}
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 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
// 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()
{
UNITY_BEGIN();
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);
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();
return UNITY_END();
}