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kachelto100370:RMax
kachelto100370:KTobi2
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freudenreichan:main

3 Commits
main ... KTobi2

Author SHA1 Message Date
kachelto100370
786aa2e6d8 working on imageinput 2025-11-25 10:53:28 +01:00
kachelto100370
58df4199b5 smal gitignore 2025-11-23 16:33:44 +01:00
kachelto100370
fb18b75b60 working on imputimage 0 test passing 2025-11-23 16:33:22 +01:00
11 changed files with 630 additions and 1064 deletions
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5
.gitignore vendored
View File

@ -2,6 +2,9 @@ mnist
runTests
*.o
*.exe
.vscode/c_cpp_properties.json
.vscode/settings.json
.vscode/launch.json
.vscode/settings.json
.vscode/settings.json
runImageInputTests
testFile.info2

18
.vscode/c_cpp_properties.json vendored
View File

@ -1,18 +0,0 @@
{
"configurations": [
{
"name": "windows-gcc-x64",
"includePath": [
"${workspaceFolder}/**"
],
"compilerPath": "C:/ProgramData/mingw64/mingw64/bin/gcc.exe",
"cStandard": "${default}",
"cppStandard": "${default}",
"intelliSenseMode": "windows-gcc-x64",
"compilerArgs": [
""
]
}
],
"version": 4
}

24
.vscode/launch.json vendored
View File

@ -1,24 +0,0 @@
{
"version": "0.2.0",
"configurations": [
{
"name": "C/C++ Runner: Debug Session",
"type": "cppdbg",
"request": "launch",
"args": [],
"stopAtEntry": false,
"externalConsole": true,
"cwd": "c:/Users/Max-R/I2Pr/repoKachelto/I2-Pr_neuronalesNetz/info2Praktikum-NeuronalesNetz",
"program": "c:/Users/Max-R/I2Pr/repoKachelto/I2-Pr_neuronalesNetz/info2Praktikum-NeuronalesNetz/build/Debug/outDebug",
"MIMode": "gdb",
"miDebuggerPath": "gdb",
"setupCommands": [
{
"description": "Enable pretty-printing for gdb",
"text": "-enable-pretty-printing",
"ignoreFailures": true
}
]
}
]
}

58
.vscode/settings.json vendored
View File

@ -1,59 +1,3 @@
{
"C_Cpp_Runner.cCompilerPath": "gcc",
"C_Cpp_Runner.cppCompilerPath": "g++",
"C_Cpp_Runner.debuggerPath": "gdb",
"C_Cpp_Runner.cStandard": "",
"C_Cpp_Runner.cppStandard": "",
"C_Cpp_Runner.msvcBatchPath": "C:/Program Files/Microsoft Visual Studio/VR_NR/Community/VC/Auxiliary/Build/vcvarsall.bat",
"C_Cpp_Runner.useMsvc": false,
"C_Cpp_Runner.warnings": [
"-Wall",
"-Wextra",
"-Wpedantic",
"-Wshadow",
"-Wformat=2",
"-Wcast-align",
"-Wconversion",
"-Wsign-conversion",
"-Wnull-dereference"
],
"C_Cpp_Runner.msvcWarnings": [
"/W4",
"/permissive-",
"/w14242",
"/w14287",
"/w14296",
"/w14311",
"/w14826",
"/w44062",
"/w44242",
"/w14905",
"/w14906",
"/w14263",
"/w44265",
"/w14928"
],
"C_Cpp_Runner.enableWarnings": true,
"C_Cpp_Runner.warningsAsError": false,
"C_Cpp_Runner.compilerArgs": [],
"C_Cpp_Runner.linkerArgs": [],
"C_Cpp_Runner.includePaths": [],
"C_Cpp_Runner.includeSearch": [
"*",
"**/*"
],
"C_Cpp_Runner.excludeSearch": [
"**/build",
"**/build/**",
"**/.*",
"**/.*/**",
"**/.vscode",
"**/.vscode/**"
],
"C_Cpp_Runner.useAddressSanitizer": false,
"C_Cpp_Runner.useUndefinedSanitizer": false,
"C_Cpp_Runner.useLeakSanitizer": false,
"C_Cpp_Runner.showCompilationTime": false,
"C_Cpp_Runner.useLinkTimeOptimization": false,
"C_Cpp_Runner.msvcSecureNoWarnings": false
"makefile.configureOnOpen": false
}

149
imageInput.c
View File

@ -1,134 +1,55 @@
#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__"
/* ----------------------------------------------------------
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 Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
GrayScaleImage readImage()
{
}
/* ----------------------------------------------------------
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;
// TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
GrayScaleImageSeries *readImages(const char *path)
{
unsigned short * numImages;
unsigned short * breiteBilder;
unsigned short * laengeBilder;
return 1;
}
GrayScaleImageSeries *series = NULL;
FILE *file = fopen(*path,"rb");
char * headOfFile;
/* ----------------------------------------------------------
3. Einzelbild lesen
---------------------------------------------------------- */
static int readSingleImage(FILE *file, GrayScaleImage *img,
unsigned short width, unsigned short height) {
img->width = width;
img->height = height;
fread(headOfFile, sizeof(FILE_HEADER_STRING),1, file); //liest den header ein und überprüft ob korrekte datei
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)
if(strcmp(headOfFile, FILE_HEADER_STRING) != 0)
return NULL;
if (!readHeader(file)) {
fclose(file);
return NULL;
// liest numIMages, breite und länge der Bilder ein
fseek(file, sizeof(FILE_HEADER_STRING), SEEK_SET);
fread(numImages, sizeof(short), 1, file);
fseek(file, sizeof(short), SEEK_CUR);
fread(breiteBilder, sizeof(short), 1, file);
fseek(file, sizeof(short), SEEK_CUR);
fread(laengeBilder, sizeof(short), 1, file);
series = malloc(*numImages * *breiteBilder * *laengeBilder * sizeof(short));
for(int i = 0; i < numImages; i++)
{
}
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;
// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
void clearSeries(GrayScaleImageSeries *series)
{
for (unsigned int i = 0; i < series->count; i++) {
free(series->images[i].buffer);
}
free(series->images);
free(series->labels);
free(series);
}

173
imageInputTests.c
View File

@ -1,98 +1,88 @@
#include "imageInput.h"
#include "unity.h"
#include <stdio.h>
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include "unity.h"
#include "imageInput.h"
/* ---------------------------------------------------------
Hilfsfunktion: Testdatei vorbereiten
--------------------------------------------------------- */
static void prepareImageFile(const char *path, unsigned int width,
unsigned int height, unsigned int numberOfImages,
unsigned char label) {
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");
if (!file)
return;
// Header
if(file != NULL)
{
const char *fileTag = "__info2_image_file_format__";
fwrite(fileTag, 1, strlen(fileTag), file);
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;
// Jedes Bild schreiben: Pixel + Label
for (unsigned int img = 0; img < numberOfImages; img++) {
fwrite(buffer, sizeof(GrayScalePixelType), width * height, file);
for(int i = 0; i < numberOfImages; i++)
{
fwrite(zeroBuffer, sizeof(GrayScalePixelType), width * height, file);
fwrite(&label, sizeof(unsigned char), 1, file);
}
free(buffer);
fclose(file);
free(zeroBuffer);
}
/* ---------------------------------------------------------
Unit Tests
--------------------------------------------------------- */
fclose(file);
}
}
void test_readImagesReturnsCorrectNumberOfImages(void) {
void test_readImagesReturnsCorrectNumberOfImages(void)
{
GrayScaleImageSeries *series = NULL;
const unsigned int expectedNumberOfImages = 2;
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_UINT(expectedNumberOfImages, series->count);
TEST_ASSERT_EQUAL_UINT16(expectedNumberOfImages, series->count);
clearSeries(series);
remove(path);
}
void test_readImagesReturnsCorrectImageWidth(void) {
void test_readImagesReturnsCorrectImageWidth(void)
{
GrayScaleImageSeries *series = NULL;
const unsigned int expectedWidth = 10;
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_UINT(2, series->count);
TEST_ASSERT_EQUAL_UINT(expectedWidth, series->images[0].width);
TEST_ASSERT_EQUAL_UINT(expectedWidth, series->images[1].width);
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_readImagesReturnsCorrectImageHeight(void) {
void test_readImagesReturnsCorrectImageHeight(void)
{
GrayScaleImageSeries *series = NULL;
const unsigned int expectedHeight = 10;
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_UINT(2, series->count);
TEST_ASSERT_EQUAL_UINT(expectedHeight, series->images[0].height);
TEST_ASSERT_EQUAL_UINT(expectedHeight, series->images[1].height);
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_readImagesReturnsCorrectLabels(void) {
void test_readImagesReturnsCorrectLabels(void)
{
const unsigned char expectedLabel = 15;
GrayScaleImageSeries *series = NULL;
@ -101,7 +91,7 @@ void test_readImagesReturnsCorrectLabels(void) {
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_NOT_NULL(series->labels);
TEST_ASSERT_EQUAL_UINT(2, series->count);
TEST_ASSERT_EQUAL_UINT16(2, series->count);
for (int i = 0; i < 2; i++) {
TEST_ASSERT_EQUAL_UINT8(expectedLabel, series->labels[i]);
}
@ -109,16 +99,19 @@ void test_readImagesReturnsCorrectLabels(void) {
remove(path);
}
void test_readImagesReturnsNullOnNotExistingPath(void) {
void test_readImagesReturnsNullOnNotExistingPath(void)
{
const char *path = "testFile.txt";
remove(path);
TEST_ASSERT_NULL(readImages(path));
}
void test_readImagesFailsOnWrongFileTag(void) {
void test_readImagesFailsOnWrongFileTag(void)
{
const char *path = "testFile.info2";
FILE *file = fopen(path, "w");
if (file != NULL) {
if(file != NULL)
{
fprintf(file, "some_tag ");
fclose(file);
TEST_ASSERT_NULL(readImages(path));
@ -126,79 +119,25 @@ void test_readImagesFailsOnWrongFileTag(void) {
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]);
void setUp(void) {
// Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
}
clearSeries(series);
remove(path);
void tearDown(void) {
// Hier kann Bereinigungsarbeit nach jedem Test durchgeführt 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);
}
/* ---------------------------------------------------------
Setup / Teardown
--------------------------------------------------------- */
void setUp(void) {}
void tearDown(void) {}
/* ---------------------------------------------------------
main()
--------------------------------------------------------- */
int main(void) {
int main()
{
UNITY_BEGIN();
printf("\n============================\nImage input "
"tests\n============================\n");
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();
}

4
makefile
View File

@ -63,4 +63,6 @@ ifeq ($(OS),Windows_NT)
else
rm -f *.o mnist runMatrixTests runNeuralNetworkTests runImageInputTests
endif
clean für windows
clean:
rm -f *.o *.exe

178
matrix.c
View File

@ -1,19 +1,13 @@
#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) {
Matrix errorMatrix = {0, 0, NULL};
return errorMatrix;
}
MatrixType *buffer =
malloc(rows * cols * sizeof(MatrixType)); // Speicher reservieren, malloc
// liefert Zeiger auf Speicher
@ -21,188 +15,54 @@ Matrix createMatrix(unsigned int rows, unsigned int cols) {
return newMatrix;
}
void clearMatrix(Matrix *matrix) {
if (matrix->buffer != NULL) {
free((*matrix).buffer);
matrix->buffer = NULL;
matrix->buffer = UNDEFINED_MATRIX_VALUE;
matrix->rows = UNDEFINED_MATRIX_VALUE;
matrix->cols = UNDEFINED_MATRIX_VALUE;
free((*matrix).buffer); // Speicher freigeben
}
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) {
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
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 ||
matrix.buffer == NULL) { // Speichergröße nicht überschreiten
return UNDEFINED_MATRIX_VALUE;
if (rowIdx >= matrix.rows ||
colIdx >= matrix.cols) { // Speichergröße nicht überschreiten
return 0;
}
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 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) {
// Ergebnismatrix
Matrix result;
const int cols1 = matrix1.cols;
const int rows1 = matrix1.rows;
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;
// Broadcasting nur bei Vektor und Matrix, Fehlermeldung bei zwei unpassender
// Matrix
if (rowsEqual == 1 && colsEqual == 1) {
const int colsEqu = (matrix1.cols == matrix2.cols) ? 1 : 0;
const int rowsEqu = (matrix1.rows == matrix2.rows) ? 1 : 0;
if(colsEqu && rowsEqu)
{
Matrix result = createMatrix(matrix1.rows, matrix1.cols);
if (result.buffer == NULL) {
return (Matrix){0, 0, NULL};
}
for (int i = 0; i < rows1; i++) {
for (int j = 0; j < cols1; 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
Matrix newMatrix = broadcastingCols(matrix1, cols2);
// add
Matrix result = createMatrix(newMatrix.rows, newMatrix.cols);
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);
}
}
return result;
} else {
Matrix newMatrix2 = broadcastingCols(matrix2, cols1);
// add
Matrix result = createMatrix(newMatrix2.rows, newMatrix2.cols);
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);
}
}
return result;
}
}
else if ((rows1 == 1 || rows2 == 1) && colsEqual == 1) {
if (rows1 == 1) {
Matrix newMatrix = broadcastingRows(matrix1, rows2);
// add
Matrix result = createMatrix(newMatrix.rows, newMatrix.cols);
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);
}
}
return result;
} else {
Matrix newMatrix2 = broadcastingRows(matrix2, rows1);
// add
Matrix result = createMatrix(newMatrix2.rows, newMatrix2.cols);
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);
}
}
return result;
}
} 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++) {
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 multiply(const Matrix matrix1, const Matrix matrix2) { return matrix1; }

5
matrix.h
View File

@ -19,11 +19,6 @@ 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);

153
neuralNetwork.c
View File

@ -1,29 +1,35 @@
#include "neuralNetwork.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <string.h>
#include "neuralNetwork.h"
#define BUFFER_SIZE 100
#define FILE_HEADER_STRING "__info2_neural_network_file_format__"
static void softmax(Matrix *matrix) {
if (matrix->cols > 0) {
static void softmax(Matrix *matrix)
{
if(matrix->cols > 0)
{
double *colSums = (double *)calloc(matrix->cols, sizeof(double));
if (colSums != NULL) {
for (int colIdx = 0; colIdx < matrix->cols; colIdx++) {
for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) {
if(colSums != NULL)
{
for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
{
for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
{
MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx));
setMatrixAt(expValue, *matrix, rowIdx, colIdx);
colSums[colIdx] += expValue;
}
}
for (int colIdx = 0; colIdx < matrix->cols; colIdx++) {
for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) {
MatrixType normalizedValue =
getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx];
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);
}
}
@ -32,13 +38,16 @@ static void softmax(Matrix *matrix) {
}
}
static void relu(Matrix *matrix) {
for (int i = 0; i < matrix->rows * matrix->cols; i++) {
static void relu(Matrix *matrix)
{
for(int i = 0; i < matrix->rows * matrix->cols; i++)
{
matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0;
}
}
static int checkFileHeader(FILE *file) {
static int checkFileHeader(FILE *file)
{
int isValid = 0;
int fileHeaderLen = strlen(FILE_HEADER_STRING);
char buffer[BUFFER_SIZE] = {0};
@ -52,7 +61,8 @@ static int checkFileHeader(FILE *file) {
return isValid;
}
static unsigned int readDimension(FILE *file) {
static unsigned int readDimension(FILE *file)
{
int dimension = 0;
if(fread(&dimension, sizeof(int), 1, file) != 1)
@ -61,20 +71,21 @@ static unsigned int readDimension(FILE *file) {
return dimension;
}
static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols) {
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)
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) {
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);
@ -82,22 +93,25 @@ static Layer readLayer(FILE *file, unsigned int inputDimension,
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 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) {
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++) {
static void assignActivations(NeuralNetwork model)
{
for(int i = 0; i < (int)model.numberOfLayers-1; i++)
{
model.layers[i].activation = relu;
}
@ -105,31 +119,36 @@ static void assignActivations(NeuralNetwork model) {
model.layers[model.numberOfLayers-1].activation = softmax;
}
NeuralNetwork loadModel(const char *path) {
NeuralNetwork loadModel(const char *path)
{
NeuralNetwork model = {NULL, 0};
FILE *file = fopen(path, "rb");
if (file != NULL) {
if (checkFileHeader(file)) {
if(file != NULL)
{
if(checkFileHeader(file))
{
unsigned int inputDimension = readDimension(file);
unsigned int outputDimension = readDimension(file);
while (inputDimension > 0 && outputDimension > 0) {
while(inputDimension > 0 && outputDimension > 0)
{
Layer layer = readLayer(file, inputDimension, outputDimension);
Layer *layerBuffer = NULL;
if (isEmptyLayer(layer)) {
if(isEmptyLayer(layer))
{
clearLayer(&layer);
clearModel(&model);
break;
}
layerBuffer = (Layer *)realloc(
model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
layerBuffer = (Layer *)realloc(model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
if(layerBuffer != NULL)
model.layers = layerBuffer;
else {
else
{
clearModel(&model);
break;
}
@ -149,16 +168,20 @@ NeuralNetwork loadModel(const char *path) {
return model;
}
static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[],
unsigned int count) {
Matrix matrix = {0, 0, NULL}; // falsch herum
static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
{
Matrix matrix = {NULL, 0, 0};
if (count > 0 && images != NULL) {
if(count > 0 && images != NULL)
{
matrix = createMatrix(images[0].height * images[0].width, count);
if (matrix.buffer != NULL) {
for (int i = 0; i < count; i++) {
for (int j = 0; j < images[i].width * images[i].height; j++) {
if(matrix.buffer != NULL)
{
for(int i = 0; i < count; i++)
{
for(int j = 0; j < images[i].width * images[i].height; j++)
{
setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i);
}
}
@ -168,11 +191,14 @@ static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[],
return matrix;
}
static Matrix forward(const NeuralNetwork model, Matrix inputBatch) {
static Matrix forward(const NeuralNetwork model, Matrix inputBatch)
{
Matrix result = inputBatch;
if (result.buffer != NULL) {
for (int i = 0; i < model.numberOfLayers; i++) {
if(result.buffer != NULL)
{
for(int i = 0; i < model.numberOfLayers; i++)
{
Matrix biasResult;
Matrix weightResult;
@ -190,19 +216,23 @@ static Matrix forward(const NeuralNetwork model, Matrix inputBatch) {
return result;
}
unsigned char *argmax(const Matrix matrix) {
unsigned char *argmax(const Matrix matrix)
{
unsigned char *maxIdx = NULL;
if (matrix.rows > 0 && matrix.cols > 0) {
if(matrix.rows > 0 && matrix.cols > 0)
{
maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols);
if (maxIdx != NULL) {
for (int colIdx = 0; colIdx < matrix.cols; colIdx++) {
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))
for(int rowIdx = 1; rowIdx < matrix.rows; rowIdx++)
{
if(getMatrixAt(matrix, rowIdx, colIdx) > getMatrixAt(matrix, maxIdx[colIdx], colIdx))
maxIdx[colIdx] = rowIdx;
}
}
@ -212,8 +242,8 @@ unsigned char *argmax(const Matrix matrix) {
return maxIdx;
}
unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
unsigned int numberOfImages) {
unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[], unsigned int numberOfImages)
{
Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages);
Matrix outputBatch = forward(model, inputBatch);
@ -224,9 +254,12 @@ unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
return result;
}
void clearModel(NeuralNetwork *model) {
if (model != NULL) {
for (int i = 0; i < model->numberOfLayers; i++) {
void clearModel(NeuralNetwork *model)
{
if(model != NULL)
{
for(int i = 0; i < model->numberOfLayers; i++)
{
clearLayer(&model->layers[i]);
}
model->layers = NULL;

187
neuralNetworkTests.c
View File

@ -1,99 +1,18 @@
#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)
{
Matrix weights;
Matrix biases;
ActivationFunctionType activation;
} Layer;
// TODO
}
typedef struct
void test_loadModelReturnsCorrectNumberOfLayers(void)
{
Layer *layers;
unsigned int numberOfLayers;
} NeuralNetwork;*/
/*Layer: Ebene im neuronalen Netzwerk, besteht aus mehreren Neuronen
Input-Layer: Eingabedatei
Hidden-Layer: verarbeiten die Daten
Output-Layer: Ergebnis
Gewichte: bestimmen, wie stark ein Eingangssignal auf ein Neuron wirkt
Dimension: Form der Matrizen für einen Layer*/
// 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
// 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);
// 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;
}
// 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);
/* 3) Für jede Layer in Reihenfolge: Gewichte (output x input), Biases (output
x 1). Zwischen Layern wird nur die nächste outputDimension (int)
geschrieben. */
for (int i = 0; i < nn.numberOfLayers; i++) {
Layer layer = nn.layers[i];
int wrows = (int)layer.weights.rows;
int wcols = (int)layer.weights.cols;
int wcount = wrows * wcols;
int bcount =
layer.biases.rows * layer.biases.cols; /* normalerweise rows * 1 */
/* Gewichte (MatrixType binär) */
if (wcount > 0 && layer.weights.buffer != NULL) {
fwrite(layer.weights.buffer, sizeof(MatrixType), (size_t)wcount, fptr);
}
/* 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_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};
@ -103,8 +22,7 @@ void test_loadModelReturnsCorrectNumberOfLayers(void) {
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[] = {{.weights=weights1, .biases=biases1}, {.weights=weights2, .biases=biases2}};
NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=2};
NeuralNetwork netUnderTest;
@ -114,12 +32,12 @@ void test_loadModelReturnsCorrectNumberOfLayers(void) {
netUnderTest = loadModel(path);
remove(path);
TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers,
netUnderTest.numberOfLayers);
TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers, netUnderTest.numberOfLayers);
clearModel(&netUnderTest);
}
void test_loadModelReturnsCorrectWeightDimensions(void) {
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};
@ -136,14 +54,13 @@ void test_loadModelReturnsCorrectWeightDimensions(void) {
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);
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) {
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};
@ -160,14 +77,13 @@ void test_loadModelReturnsCorrectBiasDimensions(void) {
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);
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) {
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};
@ -184,18 +100,15 @@ void test_loadModelReturnsCorrectWeights(void) {
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);
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) {
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};
@ -212,23 +125,21 @@ void test_loadModelReturnsCorrectBiases(void) {
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);
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) {
void test_loadModelFailsOnWrongFileTag(void)
{
const char *path = "some_nn_test_file.info2";
NeuralNetwork netUnderTest;
FILE *file = fopen(path, "wb");
if (file != NULL) {
if(file != NULL)
{
const char *fileTag = "info2_neural_network_file_format";
fwrite(fileTag, sizeof(char), strlen(fileTag), file);
@ -244,7 +155,8 @@ void test_loadModelFailsOnWrongFileTag(void) {
TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
}
void test_clearModelSetsMembersToNull(void) {
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};
@ -267,23 +179,23 @@ void test_clearModelSetsMembersToNull(void) {
TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
}
static void someActivation(Matrix *matrix) {
for (int i = 0; i < matrix->rows * matrix->cols; i++) {
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) {
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}};
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};
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};
@ -293,9 +205,8 @@ void test_predictReturnsCorrectLabels(void) {
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},
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);
@ -313,11 +224,11 @@ void tearDown(void) {
// Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
}
int main() {
int main()
{
UNITY_BEGIN();
printf("\n============================\nNeural network "
"tests\n============================\n");
printf("\n============================\nNeural network tests\n============================\n");
RUN_TEST(test_loadModelReturnsCorrectNumberOfLayers);
RUN_TEST(test_loadModelReturnsCorrectWeightDimensions);
RUN_TEST(test_loadModelReturnsCorrectBiasDimensions);