kachelto100370/info2Praktikum-NeuronalesNetz
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kachelto100370:RMax
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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
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@ -2,6 +2,9 @@ mnist
runTests runTests
*.o *.o
*.exe *.exe
.vscode/c_cpp_properties.json .vscode/settings.json
.vscode/launch.json .vscode/launch.json
.vscode/settings.json .vscode/settings.json
.vscode/settings.json
runImageInputTests
testFile.info2

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
}
]
}
]
}

58
.vscode/settings.json vendored
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@ -1,59 +1,3 @@
{ {
"C_Cpp_Runner.cCompilerPath": "gcc", "makefile.configureOnOpen": false
"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
} }

149
imageInput.c
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@ -1,134 +1,55 @@
#include "imageInput.h"
#include <stdio.h> #include <stdio.h>
#include <stdlib.h> #include <stdlib.h>
#include <string.h> #include <string.h>
#include "imageInput.h"
#define BUFFER_SIZE 100
#define FILE_HEADER_STRING "__info2_image_file_format__" #define FILE_HEADER_STRING "__info2_image_file_format__"
/* ---------------------------------------------------------- // TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
1. Header prüfen GrayScaleImage readImage()
---------------------------------------------------------- */ {
static int readHeader(FILE *file) {
char header[sizeof(FILE_HEADER_STRING)];
if (fread(header, 1, sizeof(FILE_HEADER_STRING) - 1, file) !=
sizeof(FILE_HEADER_STRING) - 1)
return 0;
header[sizeof(FILE_HEADER_STRING) - 1] = '\0';
return strcmp(header, FILE_HEADER_STRING) == 0;
} }
/* ---------------------------------------------------------- // TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
2. Meta-Daten lesen (unsigned short) GrayScaleImageSeries *readImages(const char *path)
---------------------------------------------------------- */ {
static int readMeta(FILE *file, unsigned short *count, unsigned short *width, unsigned short * numImages;
unsigned short *height) { unsigned short * breiteBilder;
if (fread(count, sizeof(unsigned short), 1, file) != 1) unsigned short * laengeBilder;
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; GrayScaleImageSeries *series = NULL;
} FILE *file = fopen(*path,"rb");
char * headOfFile;
/* ---------------------------------------------------------- fread(headOfFile, sizeof(FILE_HEADER_STRING),1, file); //liest den header ein und überprüft ob korrekte datei
3. Einzelbild lesen
---------------------------------------------------------- */ if(strcmp(headOfFile, FILE_HEADER_STRING) != 0)
static int readSingleImage(FILE *file, GrayScaleImage *img, return NULL;
unsigned short width, unsigned short height) {
img->width = width; // liest numIMages, breite und länge der Bilder ein
img->height = height; fseek(file, sizeof(FILE_HEADER_STRING), SEEK_SET);
fread(numImages, sizeof(short), 1, file);
size_t numPixels = (size_t)width * (size_t)height; fseek(file, sizeof(short), SEEK_CUR);
img->buffer = malloc(numPixels); fread(breiteBilder, sizeof(short), 1, file);
if (!img->buffer)
return 0;
if (fread(img->buffer, 1, numPixels, file) != numPixels) { fseek(file, sizeof(short), SEEK_CUR);
free(img->buffer); fread(laengeBilder, sizeof(short), 1, file);
img->buffer = NULL;
return 0;
}
return 1;
}
/* ---------------------------------------------------------- series = malloc(*numImages * *breiteBilder * *laengeBilder * sizeof(short));
4. Label lesen
---------------------------------------------------------- */
static int readLabel(FILE *file, unsigned char *label) {
return fread(label, 1, 1, file) == 1;
}
/* ---------------------------------------------------------- for(int i = 0; i < numImages; i++)
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;
return series;
} }
/* ---------------------------------------------------------- // TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
6. Speicher komplett freigeben void clearSeries(GrayScaleImageSeries *series)
---------------------------------------------------------- */ {
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);
}

291
imageInputTests.c
View File

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

4
makefile
View File

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

204
matrix.c
View File

@ -1,19 +1,13 @@
#include "matrix.h" #include "matrix.h"
#include <stdio.h>
#include <stdlib.h> #include <stdlib.h>
#include <string.h> #include <string.h>
// TODO Matrix-Funktionen implementieren
/*typedef struct { /*typedef struct {
unsigned int rows; //Zeilen unsigned int rows; //Zeilen
unsigned int cols; //Spalten unsigned int cols; //Spalten
MatrixType *buffer; //Zeiger auf Speicherbereich Reihen*Spalten MatrixType *buffer; //Zeiger auf Speicherbereich Reihen*Spalten
} Matrix;*/ } Matrix;*/
Matrix createMatrix(unsigned int rows, unsigned int cols) { Matrix createMatrix(unsigned int rows, unsigned int cols) {
if (cols == 0 || rows == 0) {
Matrix errorMatrix = {0, 0, NULL};
return errorMatrix;
}
MatrixType *buffer = MatrixType *buffer =
malloc(rows * cols * sizeof(MatrixType)); // Speicher reservieren, malloc malloc(rows * cols * sizeof(MatrixType)); // Speicher reservieren, malloc
// liefert Zeiger auf Speicher // liefert Zeiger auf Speicher
@ -21,188 +15,54 @@ Matrix createMatrix(unsigned int rows, unsigned int cols) {
return newMatrix; return newMatrix;
} }
void clearMatrix(Matrix *matrix) { void clearMatrix(Matrix *matrix) {
matrix->buffer = UNDEFINED_MATRIX_VALUE;
if (matrix->buffer != NULL) { matrix->rows = UNDEFINED_MATRIX_VALUE;
free((*matrix).buffer); matrix->cols = UNDEFINED_MATRIX_VALUE;
matrix->buffer = NULL; free((*matrix).buffer); // Speicher freigeben
}
matrix->rows = 0;
matrix->cols = 0;
} }
void setMatrixAt(const MatrixType value, Matrix matrix, void setMatrixAt(const MatrixType value, Matrix matrix,
const unsigned int rowIdx, // Kopie der Matrix wird übergeben const unsigned int rowIdx, // Kopie der Matrix wird übergeben
const unsigned int colIdx) { const unsigned int colIdx) {
if (rowIdx >= matrix.rows || colIdx >= matrix.cols) { matrix.buffer[rowIdx * matrix.cols + colIdx] =
// Speichergröße nicht überschreiten value; // rowIdx * matrix.cols -> Beginn der Zeile colIdx ->Spalte
return; // innerhalb der Zeile
}
matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
// rowIdx * matrix.cols -> Beginn der Zeile colIdx ->Spalte
// innerhalb der Zeile
} }
MatrixType getMatrixAt(const Matrix matrix, MatrixType getMatrixAt(const Matrix matrix,
unsigned int rowIdx, // Kopie der Matrix wird übergeben unsigned int rowIdx, // Kopie der Matrix wird übergeben
unsigned int colIdx) { unsigned int colIdx) {
if (rowIdx >= matrix.rows || colIdx >= matrix.cols || if (rowIdx >= matrix.rows ||
matrix.buffer == NULL) { // Speichergröße nicht überschreiten colIdx >= matrix.cols) { // Speichergröße nicht überschreiten
return UNDEFINED_MATRIX_VALUE; return 0;
} }
MatrixType value = matrix.buffer[rowIdx * matrix.cols + colIdx]; MatrixType value = matrix.buffer[rowIdx * matrix.cols + colIdx];
return value; 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) { Matrix add(const Matrix matrix1, const Matrix matrix2) {
Matrix result;
// Ergebnismatrix const int cols1 = matrix1.cols;
Matrix result; const int rows1 = matrix1.rows;
const int cols1 = matrix1.cols; const int cols2 = matrix2.cols;
const int rows1 = matrix1.rows; const int rows2 = matrix2.rows;
const int cols2 = matrix2.cols; const int colsEqu = (matrix1.cols == matrix2.cols) ? 1 : 0;
const int rows2 = matrix2.rows; const int rowsEqu = (matrix1.rows == matrix2.rows) ? 1 : 0;
if(colsEqu && rowsEqu)
const int rowsEqual = (matrix1.rows == matrix2.rows) ? 1 : 0; {
const int colsEqual = (matrix1.cols == matrix2.cols) ? 1 : 0; Matrix result = createMatrix(matrix1.rows, matrix1.cols);
for(int i = 0; i < rows1; i++)
// Broadcasting nur bei Vektor und Matrix, Fehlermeldung bei zwei unpassender {
// Matrix for (int j = 0; j < cols1; j++)
if (rowsEqual == 1 && colsEqual == 1) { {
Matrix result = createMatrix(matrix1.rows, matrix1.cols); int valueM1 = getMatrixAt(matrix1, i, j);
if (result.buffer == NULL) { int valueM2 =getMatrixAt(matrix2, i, j);
return (Matrix){0, 0, NULL}; int sum = valueM1 + valueM2;
} setMatrixAt(sum, result,i,j);
for (int i = 0; i < rows1; i++) { }
for (int j = 0; j < cols1; j++) {
int valueM1 = getMatrixAt(matrix1, i, j);
int valueM2 = getMatrixAt(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) { return result;
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); unsigned int colIdx);
MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx,
unsigned int colIdx); 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 add(const Matrix matrix1, const Matrix matrix2);
Matrix multiply(const Matrix matrix1, const Matrix matrix2); Matrix multiply(const Matrix matrix1, const Matrix matrix2);

431
neuralNetwork.c
View File

@ -1,235 +1,268 @@
#include "neuralNetwork.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h> #include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <string.h> #include <string.h>
#include "neuralNetwork.h"
#define BUFFER_SIZE 100 #define BUFFER_SIZE 100
#define FILE_HEADER_STRING "__info2_neural_network_file_format__" #define FILE_HEADER_STRING "__info2_neural_network_file_format__"
static void softmax(Matrix *matrix) { static void softmax(Matrix *matrix)
if (matrix->cols > 0) { {
double *colSums = (double *)calloc(matrix->cols, sizeof(double)); if(matrix->cols > 0)
{
double *colSums = (double *)calloc(matrix->cols, sizeof(double));
if (colSums != NULL) { if(colSums != NULL)
for (int colIdx = 0; colIdx < matrix->cols; colIdx++) { {
for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) { for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx)); {
setMatrixAt(expValue, *matrix, rowIdx, colIdx); for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
colSums[colIdx] += expValue; {
MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx));
setMatrixAt(expValue, *matrix, rowIdx, colIdx);
colSums[colIdx] += expValue;
}
}
for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
{
for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
{
MatrixType normalizedValue = getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx];
setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx);
}
}
free(colSums);
} }
} }
}
for (int colIdx = 0; colIdx < matrix->cols; colIdx++) { static void relu(Matrix *matrix)
for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) { {
MatrixType normalizedValue = for(int i = 0; i < matrix->rows * matrix->cols; i++)
getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx]; {
setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx); 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);
}
} }
} fclose(file);
free(colSums);
assignActivations(model);
} }
}
return model;
} }
static void relu(Matrix *matrix) { static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
for (int i = 0; i < matrix->rows * matrix->cols; i++) { {
matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0; Matrix matrix = {NULL, 0, 0};
}
}
static int checkFileHeader(FILE *file) { if(count > 0 && images != NULL)
int isValid = 0; {
int fileHeaderLen = strlen(FILE_HEADER_STRING); matrix = createMatrix(images[0].height * images[0].width, count);
char buffer[BUFFER_SIZE] = {0};
if (BUFFER_SIZE - 1 < fileHeaderLen) if(matrix.buffer != NULL)
fileHeaderLen = BUFFER_SIZE - 1; {
for(int i = 0; i < count; i++)
if (fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen) {
isValid = strcmp(buffer, FILE_HEADER_STRING) == 0; for(int j = 0; j < images[i].width * images[i].height; j++)
{
return isValid; setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i);
} }
}
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( return matrix;
model.layers, (model.numberOfLayers + 1) * sizeof(Layer)); }
if (layerBuffer != NULL) static Matrix forward(const NeuralNetwork model, Matrix inputBatch)
model.layers = layerBuffer; {
else { Matrix result = inputBatch;
clearModel(&model);
break; 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 result;
}
return model;
} }
static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned char *argmax(const Matrix matrix)
unsigned int count) { {
Matrix matrix = {0, 0, NULL}; // falsch herum unsigned char *maxIdx = NULL;
if (count > 0 && images != NULL) { if(matrix.rows > 0 && matrix.cols > 0)
matrix = createMatrix(images[0].height * images[0].width, count); {
maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols);
if (matrix.buffer != NULL) { if(maxIdx != NULL)
for (int i = 0; i < count; i++) { {
for (int j = 0; j < images[i].width * images[i].height; j++) { for(int colIdx = 0; colIdx < matrix.cols; colIdx++)
setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i); {
maxIdx[colIdx] = 0;
for(int rowIdx = 1; rowIdx < matrix.rows; rowIdx++)
{
if(getMatrixAt(matrix, rowIdx, colIdx) > getMatrixAt(matrix, maxIdx[colIdx], colIdx))
maxIdx[colIdx] = rowIdx;
}
}
} }
}
} }
}
return matrix; return maxIdx;
} }
static Matrix forward(const NeuralNetwork model, Matrix inputBatch) { unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[], unsigned int numberOfImages)
Matrix result = inputBatch; {
Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages);
Matrix outputBatch = forward(model, inputBatch);
if (result.buffer != NULL) { unsigned char *result = argmax(outputBatch);
for (int i = 0; i < model.numberOfLayers; i++) {
Matrix biasResult; clearMatrix(&outputBatch);
Matrix weightResult;
return result;
weightResult = multiply(model.layers[i].weights, result);
clearMatrix(&result);
biasResult = add(model.layers[i].biases, weightResult);
clearMatrix(&weightResult);
if (model.layers[i].activation != NULL)
model.layers[i].activation(&biasResult);
result = biasResult;
}
}
return result;
} }
unsigned char *argmax(const Matrix matrix) { void clearModel(NeuralNetwork *model)
unsigned char *maxIdx = NULL; {
if(model != NULL)
if (matrix.rows > 0 && matrix.cols > 0) { {
maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols); for(int i = 0; i < model->numberOfLayers; i++)
{
if (maxIdx != NULL) { clearLayer(&model->layers[i]);
for (int colIdx = 0; colIdx < matrix.cols; colIdx++) {
maxIdx[colIdx] = 0;
for (int rowIdx = 1; rowIdx < matrix.rows; rowIdx++) {
if (getMatrixAt(matrix, rowIdx, colIdx) >
getMatrixAt(matrix, maxIdx[colIdx], colIdx))
maxIdx[colIdx] = rowIdx;
} }
} model->layers = NULL;
model->numberOfLayers = 0;
} }
}
return maxIdx;
}
unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
unsigned int numberOfImages) {
Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages);
Matrix outputBatch = forward(model, inputBatch);
unsigned char *result = argmax(outputBatch);
clearMatrix(&outputBatch);
return result;
}
void clearModel(NeuralNetwork *model) {
if (model != NULL) {
for (int i = 0; i < model->numberOfLayers; i++) {
clearLayer(&model->layers[i]);
}
model->layers = NULL;
model->numberOfLayers = 0;
}
} }

505
neuralNetworkTests.c
View File

@ -1,331 +1,242 @@
#include "neuralNetwork.h"
#include "unity.h"
#include <math.h>
#include <stdio.h> #include <stdio.h>
#include <stdlib.h> #include <stdlib.h>
#include <string.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; // TODO
Matrix biases; }
ActivationFunctionType activation;
} Layer;
typedef struct void test_loadModelReturnsCorrectNumberOfLayers(void)
{ {
Layer *layers; const char *path = "some__nn_test_file.info2";
unsigned int numberOfLayers; MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
} NeuralNetwork;*/ 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 NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=2};
Input-Layer: Eingabedatei NeuralNetwork netUnderTest;
Hidden-Layer: verarbeiten die Daten
Output-Layer: Ergebnis
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 void test_loadModelReturnsCorrectWeightDimensions(void)
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) { {
FILE *fptr = fopen(path, "wb"); // Binärdatei zum Schreiben öffnen const char *path = "some__nn_test_file.info2";
if (fptr == NULL) MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
return; // file konnte nicht geöffnet werden 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 NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
// Dateiformat NeuralNetwork netUnderTest;
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 prepareNeuralNetworkFile(path, expectedNet);
if (nn.numberOfLayers == 0) {
int zero = 0;
fwrite(&zero, sizeof(int), 1, fptr);
fclose(fptr);
return;
}
// Layer 0, inputDimension: Anzahl Input-Neuronen, outputDimension: Anzahl netUnderTest = loadModel(path);
// Output-Neuronen remove(path);
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 TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
x 1). Zwischen Layern wird nur die nächste outputDimension (int) TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows);
geschrieben. */ TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols);
for (int i = 0; i < nn.numberOfLayers; i++) { clearModel(&netUnderTest);
Layer layer = nn.layers[i]; }
int wrows = (int)layer.weights.rows; void test_loadModelReturnsCorrectBiasDimensions(void)
int wcols = (int)layer.weights.cols; {
int wcount = wrows * wcols; const char *path = "some__nn_test_file.info2";
int bcount = MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
layer.biases.rows * layer.biases.cols; /* normalerweise rows * 1 */ 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) */ NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
if (wcount > 0 && layer.weights.buffer != NULL) { NeuralNetwork netUnderTest;
fwrite(layer.weights.buffer, sizeof(MatrixType), (size_t)wcount, fptr);
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) */ netUnderTest = loadModel(path);
if (bcount > 0 && layer.biases.buffer != NULL) {
fwrite(layer.biases.buffer, sizeof(MatrixType), (size_t)bcount, fptr); 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) { void test_predictReturnsCorrectLabels(void)
const char *path = "some__nn_test_file.info2"; {
MatrixType buffer1[] = {1, 2, 3, 4, 5, 6}; const unsigned char expectedLabels[] = {4, 2};
MatrixType buffer2[] = {1, 2, 3, 4, 5, 6}; GrayScalePixelType imageBuffer1[] = {10, 30, 25, 17};
Matrix weights1 = {.buffer = buffer1, .rows = 3, .cols = 2}; GrayScalePixelType imageBuffer2[] = {20, 40, 10, 128};
Matrix weights2 = {.buffer = buffer2, .rows = 2, .cols = 3}; GrayScaleImage inputImages[] = {{.buffer=imageBuffer1, .width=2, .height=2}, {.buffer=imageBuffer2, .width=2, .height=2}};
MatrixType buffer3[] = {1, 2, 3}; MatrixType weightsBuffer1[] = {1, -2, 3, -4, 5, -6, 7, -8};
MatrixType buffer4[] = {1, 2}; MatrixType weightsBuffer2[] = {-9, 10, 11, 12, 13, 14};
Matrix biases1 = {.buffer = buffer3, .rows = 3, .cols = 1}; MatrixType weightsBuffer3[] = {-15, 16, 17, 18, -19, 20, 21, 22, 23, -24, 25, 26, 27, -28, -29};
Matrix biases2 = {.buffer = buffer4, .rows = 2, .cols = 1}; Matrix weights1 = {.buffer=weightsBuffer1, .rows=2, .cols=4};
Layer layers[] = {{.weights = weights1, .biases = biases1}, Matrix weights2 = {.buffer=weightsBuffer2, .rows=3, .cols=2};
{.weights = weights2, .biases = biases2}}; Matrix weights3 = {.buffer=weightsBuffer3, .rows=5, .cols=3};
MatrixType biasBuffer1[] = {200, 0};
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 2}; MatrixType biasBuffer2[] = {0, -100, 0};
NeuralNetwork netUnderTest; MatrixType biasBuffer3[] = {0, -1000, 0, 2000, 0};
Matrix biases1 = {.buffer=biasBuffer1, .rows=2, .cols=1};
prepareNeuralNetworkFile(path, expectedNet); Matrix biases2 = {.buffer=biasBuffer2, .rows=3, .cols=1};
Matrix biases3 = {.buffer=biasBuffer3, .rows=5, .cols=1};
netUnderTest = loadModel(path); Layer layers[] = {{.weights=weights1, .biases=biases1, .activation=someActivation}, \
remove(path); {.weights=weights2, .biases=biases2, .activation=someActivation}, \
{.weights=weights3, .biases=biases3, .activation=someActivation}};
TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers, NeuralNetwork netUnderTest = {.layers=layers, .numberOfLayers=3};
netUnderTest.numberOfLayers); unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2);
clearModel(&netUnderTest); TEST_ASSERT_NOT_NULL(predictedLabels);
} int n = (int)(sizeof(expectedLabels) / sizeof(expectedLabels[0]));
TEST_ASSERT_EQUAL_UINT8_ARRAY(expectedLabels, predictedLabels, n);
void test_loadModelReturnsCorrectWeightDimensions(void) { free(predictedLabels);
const char *path = "some__nn_test_file.info2";
MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
MatrixType biasBuffer[] = {7, 8, 9};
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
Layer layers[] = {{.weights = weights, .biases = biases}};
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
NeuralNetwork netUnderTest;
prepareNeuralNetworkFile(path, expectedNet);
netUnderTest = loadModel(path);
remove(path);
TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows,
netUnderTest.layers[0].weights.rows);
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols,
netUnderTest.layers[0].weights.cols);
clearModel(&netUnderTest);
}
void test_loadModelReturnsCorrectBiasDimensions(void) {
const char *path = "some__nn_test_file.info2";
MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
MatrixType biasBuffer[] = {7, 8, 9};
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
Layer layers[] = {{.weights = weights, .biases = biases}};
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
NeuralNetwork netUnderTest;
prepareNeuralNetworkFile(path, expectedNet);
netUnderTest = loadModel(path);
remove(path);
TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.rows,
netUnderTest.layers[0].biases.rows);
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.cols,
netUnderTest.layers[0].biases.cols);
clearModel(&netUnderTest);
}
void test_loadModelReturnsCorrectWeights(void) {
const char *path = "some__nn_test_file.info2";
MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
MatrixType biasBuffer[] = {7, 8, 9};
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
Layer layers[] = {{.weights = weights, .biases = biases}};
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
NeuralNetwork netUnderTest;
prepareNeuralNetworkFile(path, expectedNet);
netUnderTest = loadModel(path);
remove(path);
TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows,
netUnderTest.layers[0].weights.rows);
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols,
netUnderTest.layers[0].weights.cols);
int n =
netUnderTest.layers[0].weights.rows * netUnderTest.layers[0].weights.cols;
TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].weights.buffer,
netUnderTest.layers[0].weights.buffer, n);
clearModel(&netUnderTest);
}
void test_loadModelReturnsCorrectBiases(void) {
const char *path = "some__nn_test_file.info2";
MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
MatrixType biasBuffer[] = {7, 8, 9};
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
Layer layers[] = {{.weights = weights, .biases = biases}};
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
NeuralNetwork netUnderTest;
prepareNeuralNetworkFile(path, expectedNet);
netUnderTest = loadModel(path);
remove(path);
TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows,
netUnderTest.layers[0].weights.rows);
TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols,
netUnderTest.layers[0].weights.cols);
int n =
netUnderTest.layers[0].biases.rows * netUnderTest.layers[0].biases.cols;
TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].biases.buffer,
netUnderTest.layers[0].biases.buffer, n);
clearModel(&netUnderTest);
}
void test_loadModelFailsOnWrongFileTag(void) {
const char *path = "some_nn_test_file.info2";
NeuralNetwork netUnderTest;
FILE *file = fopen(path, "wb");
if (file != NULL) {
const char *fileTag = "info2_neural_network_file_format";
fwrite(fileTag, sizeof(char), strlen(fileTag), file);
fclose(file);
}
netUnderTest = loadModel(path);
remove(path);
TEST_ASSERT_NULL(netUnderTest.layers);
TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
}
void test_clearModelSetsMembersToNull(void) {
const char *path = "some__nn_test_file.info2";
MatrixType weightBuffer[] = {1, 2, 3, 4, 5, 6};
Matrix weights = {.buffer = weightBuffer, .rows = 3, .cols = 2};
MatrixType biasBuffer[] = {7, 8, 9};
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
Layer layers[] = {{.weights = weights, .biases = biases}};
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
NeuralNetwork netUnderTest;
prepareNeuralNetworkFile(path, expectedNet);
netUnderTest = loadModel(path);
remove(path);
TEST_ASSERT_NOT_NULL(netUnderTest.layers);
TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0);
clearModel(&netUnderTest);
TEST_ASSERT_NULL(netUnderTest.layers);
TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers);
}
static void someActivation(Matrix *matrix) {
for (int i = 0; i < matrix->rows * matrix->cols; i++) {
matrix->buffer[i] = fabs(matrix->buffer[i]);
}
}
void test_predictReturnsCorrectLabels(void) {
const unsigned char expectedLabels[] = {4, 2};
GrayScalePixelType imageBuffer1[] = {10, 30, 25, 17};
GrayScalePixelType imageBuffer2[] = {20, 40, 10, 128};
GrayScaleImage inputImages[] = {
{.buffer = imageBuffer1, .width = 2, .height = 2},
{.buffer = imageBuffer2, .width = 2, .height = 2}};
MatrixType weightsBuffer1[] = {1, -2, 3, -4, 5, -6, 7, -8};
MatrixType weightsBuffer2[] = {-9, 10, 11, 12, 13, 14};
MatrixType weightsBuffer3[] = {-15, 16, 17, 18, -19, 20, 21, 22,
23, -24, 25, 26, 27, -28, -29};
Matrix weights1 = {.buffer = weightsBuffer1, .rows = 2, .cols = 4};
Matrix weights2 = {.buffer = weightsBuffer2, .rows = 3, .cols = 2};
Matrix weights3 = {.buffer = weightsBuffer3, .rows = 5, .cols = 3};
MatrixType biasBuffer1[] = {200, 0};
MatrixType biasBuffer2[] = {0, -100, 0};
MatrixType biasBuffer3[] = {0, -1000, 0, 2000, 0};
Matrix biases1 = {.buffer = biasBuffer1, .rows = 2, .cols = 1};
Matrix biases2 = {.buffer = biasBuffer2, .rows = 3, .cols = 1};
Matrix biases3 = {.buffer = biasBuffer3, .rows = 5, .cols = 1};
Layer layers[] = {
{.weights = weights1, .biases = biases1, .activation = someActivation},
{.weights = weights2, .biases = biases2, .activation = someActivation},
{.weights = weights3, .biases = biases3, .activation = someActivation}};
NeuralNetwork netUnderTest = {.layers = layers, .numberOfLayers = 3};
unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2);
TEST_ASSERT_NOT_NULL(predictedLabels);
int n = (int)(sizeof(expectedLabels) / sizeof(expectedLabels[0]));
TEST_ASSERT_EQUAL_UINT8_ARRAY(expectedLabels, predictedLabels, n);
free(predictedLabels);
} }
void setUp(void) { void setUp(void) {
// Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
} }
void tearDown(void) { void tearDown(void) {
// Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
} }
int main() { int main()
UNITY_BEGIN(); {
UNITY_BEGIN();
printf("\n============================\nNeural network " printf("\n============================\nNeural network tests\n============================\n");
"tests\n============================\n"); RUN_TEST(test_loadModelReturnsCorrectNumberOfLayers);
RUN_TEST(test_loadModelReturnsCorrectNumberOfLayers); RUN_TEST(test_loadModelReturnsCorrectWeightDimensions);
RUN_TEST(test_loadModelReturnsCorrectWeightDimensions); RUN_TEST(test_loadModelReturnsCorrectBiasDimensions);
RUN_TEST(test_loadModelReturnsCorrectBiasDimensions); RUN_TEST(test_loadModelReturnsCorrectWeights);
RUN_TEST(test_loadModelReturnsCorrectWeights); RUN_TEST(test_loadModelReturnsCorrectBiases);
RUN_TEST(test_loadModelReturnsCorrectBiases); RUN_TEST(test_loadModelFailsOnWrongFileTag);
RUN_TEST(test_loadModelFailsOnWrongFileTag); RUN_TEST(test_clearModelSetsMembersToNull);
RUN_TEST(test_clearModelSetsMembersToNull); RUN_TEST(test_predictReturnsCorrectLabels);
RUN_TEST(test_predictReturnsCorrectLabels);
return UNITY_END(); return UNITY_END();
} }