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5
.gitignore vendored
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@ -1,7 +1,4 @@
mnist
runTests
*.o
*.exe
.vscode/c_cpp_properties.json
.vscode/launch.json
.vscode/settings.json
*.exe

18
.vscode/c_cpp_properties.json vendored
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@ -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
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@ -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
}
]
}
]
}

59
.vscode/settings.json vendored
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@ -1,59 +0,0 @@
{
"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
}

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

140
imageInput.c
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@ -1,134 +1,22 @@
#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
// TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
GrayScaleImageSeries *readImages(const char *path)
{
GrayScaleImageSeries *series = NULL;
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;
}
/* ----------------------------------------------------------
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);
}
// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
void clearSeries(GrayScaleImageSeries *series)
{
}

291
imageInputTests.c
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@ -1,204 +1,143 @@
#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) {
FILE *file = fopen(path, "wb");
if (!file)
return;
// Header
const char *fileTag = "__info2_image_file_format__";
fwrite(fileTag, 1, strlen(fileTag), file);
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");
// 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(file != NULL)
{
const char *fileTag = "__info2_image_file_format__";
GrayScalePixelType *zeroBuffer = (GrayScalePixelType *)calloc(numberOfImages * width * height, sizeof(GrayScalePixelType));
// 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;
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);
// 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);
}
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);
}
fclose(file);
}
}
/* ---------------------------------------------------------
Unit Tests
--------------------------------------------------------- */
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_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_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_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_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_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_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_UINT(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_UINT16(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);
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);
void test_readImagesReturnsNullOnNotExistingPath(void)
{
const char *path = "testFile.txt";
remove(path);
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 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);
}
/* ---------------------------------------------------------
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 setUp(void) {
// Falls notwendig, kann hier Vorbereitungsarbeit gemacht 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();
void tearDown(void) {
// Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
}
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();
}

2
makefile
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@ -63,4 +63,4 @@ ifeq ($(OS),Windows_NT)
else
rm -f *.o mnist runMatrixTests runNeuralNetworkTests runImageInputTests
endif

219
matrix.c
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@ -1,208 +1,35 @@
#include "matrix.h"
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "matrix.h"
/*typedef struct {
unsigned int rows; //Zeilen
unsigned int cols; //Spalten
MatrixType *buffer; //Zeiger auf Speicherbereich Reihen*Spalten
} Matrix;*/
// TODO Matrix-Funktionen implementieren
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
Matrix newMatrix = {rows, cols, buffer}; // neue Matrix nach struct
return newMatrix;
}
void clearMatrix(Matrix *matrix) {
if (matrix->buffer != NULL) {
free((*matrix).buffer);
matrix->buffer = NULL;
}
matrix->rows = 0;
matrix->cols = 0;
Matrix createMatrix(unsigned int rows, unsigned int cols)
{
}
void setMatrixAt(const MatrixType value, Matrix matrix,
const unsigned int rowIdx, // Kopie der Matrix wird übergeben
const unsigned int colIdx) {
if (rowIdx >= matrix.rows || colIdx >= matrix.cols) {
// Speichergröße nicht überschreiten
return;
}
matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
// rowIdx * matrix.cols -> Beginn der Zeile colIdx ->Spalte
// innerhalb der Zeile
void clearMatrix(Matrix *matrix)
{
}
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;
}
MatrixType value = matrix.buffer[rowIdx * matrix.cols + colIdx];
return value;
void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
{
}
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) {
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++) {
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;
MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
{
}
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 add(const Matrix matrix1, const Matrix matrix2)
{
}
Matrix multiply(const Matrix matrix1, const Matrix matrix2)
{
}

17
matrix.h
View File

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

29
neuralN_readFiles
View File

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

431
neuralNetwork.c
View File

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

505
neuralNetworkTests.c
View File

@ -1,331 +1,242 @@
#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;*/
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: Ebene im neuronalen Netzwerk, besteht aus mehreren Neuronen
Input-Layer: Eingabedatei
Hidden-Layer: verarbeiten die Daten
Output-Layer: Ergebnis
NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=2};
NeuralNetwork netUnderTest;
prepareNeuralNetworkFile(path, expectedNet);
Gewichte: bestimmen, wie stark ein Eingangssignal auf ein Neuron wirkt
netUnderTest = loadModel(path);
remove(path);
Dimension: Form der Matrizen für einen Layer*/
TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers, netUnderTest.numberOfLayers);
clearModel(&netUnderTest);
}
// 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
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}};
// 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);
NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
NeuralNetwork netUnderTest;
// 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;
}
prepareNeuralNetworkFile(path, expectedNet);
// 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);
netUnderTest = loadModel(path);
remove(path);
/* 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];
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);
}
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 */
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}};
/* Gewichte (MatrixType binär) */
if (wcount > 0 && layer.weights.buffer != NULL) {
fwrite(layer.weights.buffer, sizeof(MatrixType), (size_t)wcount, fptr);
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);
}
/* Biases (MatrixType binär) */
if (bcount > 0 && layer.biases.buffer != NULL) {
fwrite(layer.biases.buffer, sizeof(MatrixType), (size_t)bcount, 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]);
}
/* 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};
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 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();
}