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13 Commits
39 changed files with 273 additions and 149 deletions
+2 -59
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@@ -1,61 +1,4 @@
# ---> C
# Prerequisites
*.d
# Object files
mnist
runTests
*.o
*.ko
*.obj
*.elf
# Linker output
*.ilk
*.map
*.exp
# Precompiled Headers
*.gch
*.pch
# Libraries
*.lib
*.la
*.lo
# Shared objects (inc. Windows DLLs)
*.dll
*.so
*.so.*
*.dylib
# Executables
*.exe
*.out
*.app
*.i*86
*.x86_64
*.hex
Startcode/mnist
Startcode/runTests
# Debug files
*.dSYM/
*.su
*.idb
*.pdb
# Kernel Module Compile Results
*.mod*
*.cmd
.tmp_versions/
modules.order
Module.symvers
Mkfile.old
dkms.conf
# IDE folders
.vscode/
.idea/
# macOS
.DS_Store
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-22
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@@ -1,22 +0,0 @@
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "imageInput.h"
#define BUFFER_SIZE 100
#define FILE_HEADER_STRING "__info2_image_file_format__"
// TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
// TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
GrayScaleImageSeries *readImages(const char *path)
{
GrayScaleImageSeries *series = NULL;
return series;
}
// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
void clearSeries(GrayScaleImageSeries *series)
{
}
-35
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@@ -1,35 +0,0 @@
#include <stdlib.h>
#include <string.h>
#include "matrix.h"
// TODO Matrix-Funktionen implementieren
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 add(const Matrix matrix1, const Matrix matrix2)
{
}
Matrix multiply(const Matrix matrix1, const Matrix matrix2)
{
}
+97
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@@ -0,0 +1,97 @@
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "imageInput.h"
#define BUFFER_SIZE 100
#define FILE_HEADER_STRING "__info2_image_file_format__"
#define FILE_HEADER_SIZE (sizeof(FILE_HEADER_STRING)-1)
// TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
FILE *checkFile(const char *path)
{
FILE *datei = fopen(path,"rb");
if (datei == NULL)
{
perror("Datei konnte nicht geoeffnet werden");
return NULL;
}
char buffer[FILE_HEADER_SIZE+1];
if (fread(buffer,1,FILE_HEADER_SIZE,datei)!=FILE_HEADER_SIZE)
{
perror("Header konnte nicht eingelessen werden");
fclose(datei);
return NULL;
}
buffer[FILE_HEADER_SIZE] = '\0';
if (strcmp(buffer,FILE_HEADER_STRING)!=0)
{
printf("Falscher Dateikopf");
//printf("\n%s",buffer);
//printf("\n%s",FILE_HEADER_STRING);
//printf("\n%d",strcmp(buffer,FILE_HEADER_STRING));
fclose(datei);
return NULL;
}
return datei;
}
// TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
GrayScaleImageSeries *readImages(const char *path)
{
FILE *datei = checkFile(path);
if (datei==NULL)
{
return NULL;
}
unsigned short image_count, width, height;
fread(&image_count,1,sizeof(unsigned short),datei);
fread(&width,1,sizeof(unsigned short),datei);
fread(&height,1,sizeof(unsigned short),datei);
//printf("%u Bilder und %u mal %u",image_count,width,height);
GrayScaleImageSeries *series = NULL;
series = malloc(sizeof(GrayScaleImageSeries));
series->count = image_count;
series->images = malloc(image_count*sizeof(GrayScaleImage));
series->labels = malloc(image_count*sizeof(unsigned char));
for(unsigned short i = 0;i<image_count;i++)
{
series->images[i].buffer = malloc(width*height);
}
for(unsigned short i = 0;i<image_count;i++)
{
for (unsigned int j=0;j<(width*height);j++)
{
fread(&series->images[i].buffer[j],1,1,datei);
}
fread(&series->labels[i],1,1,datei);
//printf("%d\n",series->labels[i]);
}
fclose(datei);
return series;
}
// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
void clearSeries(GrayScaleImageSeries *series)
{
if(series == NULL)
{
printf("Serie nicht vorhanden\n");
return;
}
unsigned short anzahl = series->count;
for(unsigned short i = 0;i<anzahl;i++)
{
free(series->images[i].buffer );
}
free(series->images);
free(series->labels);
free(series);
printf("Serie freigegeben\n");
return;
}
+1 -1
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@@ -19,5 +19,5 @@ typedef struct
GrayScaleImageSeries *readImages(const char *path);
void clearSeries(GrayScaleImageSeries *series);
FILE *checkFile(const char *path);
#endif
@@ -54,7 +54,7 @@ void test_readImagesReturnsCorrectImageWidth(void)
GrayScaleImageSeries *series = NULL;
const unsigned short expectedWidth = 10;
const char *path = "testFile.info2";
prepareImageFile(path, expectedWidth, 8, 2, 1);
prepareImageFile(path, 8, expectedWidth, 2, 1);
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_NOT_NULL(series->images);
@@ -70,7 +70,7 @@ void test_readImagesReturnsCorrectImageHeight(void)
GrayScaleImageSeries *series = NULL;
const unsigned short expectedHeight = 10;
const char *path = "testFile.info2";
prepareImageFile(path, 8, expectedHeight, 2, 1);
prepareImageFile(path, expectedHeight, 8, 2, 1);
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_NOT_NULL(series->images);
+3
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@@ -6,6 +6,8 @@
int main(int argc, char *argv[])
{
readImages("mnist_test.info2");
const unsigned int windowWidth = 800;
const unsigned int windowHeight = 600;
@@ -65,4 +67,5 @@ int main(int argc, char *argv[])
}
return exitCode;
}
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+107
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@@ -0,0 +1,107 @@
#include <stdlib.h>
#include <string.h>
#include "matrix.h"
// TODO Matrix-Funktionen implementieren
// Matrix erzeugen
Matrix createMatrix(unsigned int rows, unsigned int cols)
{
Matrix matrix;
matrix.buffer = NULL;
matrix.rows = 0;
matrix.cols = 0;
// Wenn die Dimensionen gültig sind, Speicher reservieren
if (rows > 0 && cols > 0)
{
matrix.buffer = (MatrixType *)malloc(rows * cols * sizeof(MatrixType));
if (matrix.buffer != NULL)
{
matrix.rows = rows;
matrix.cols = cols;
}
}
return matrix;
}
// Matrix Speicher freigeben
void clearMatrix(Matrix *matrix)
{
if (matrix->buffer != NULL)
{
free(matrix->buffer);
matrix->buffer = NULL;
}
matrix->rows = 0;
matrix->cols = 0;
}
// Wert setzen
void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
{
if (rowIdx < matrix.rows && colIdx < matrix.cols)
{
matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
}
}
// Wert auslesen
MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
{
if (rowIdx < matrix.rows && colIdx < matrix.cols)
{
return matrix.buffer[rowIdx * matrix.cols + colIdx];
}
return 0; // Fallback
}
// Matrizen addieren
Matrix add(const Matrix m1, const Matrix m2)
{
if (m1.rows != m2.rows || m1.cols != m2.cols)
{
return createMatrix(0, 0); // Falls Matrix-Dimensionen nicht passen
}
Matrix result = createMatrix(m1.rows, m1.cols);
if (result.buffer == NULL) return result;
for (unsigned int r = 0; r < m1.rows; r++)
{
for (unsigned int c = 0; c < m1.cols; c++)
{
result.buffer[r * m1.cols + c] =
getMatrixAt(m1, r, c) + getMatrixAt(m2, r, c);
}
}
return result;
}
// Matrizen multiplizieren
Matrix multiply(const Matrix m1, const Matrix m2)
{
if (m1.cols != m2.rows)
{
return createMatrix(0, 0); // Falls Matrix-Dimensionen nicht passen
}
Matrix result = createMatrix(m1.rows, m2.cols);
if (result.buffer == NULL) return result;
for (unsigned int r = 0; r < m1.rows; r++)
{
for (unsigned int c = 0; c < m2.cols; c++)
{
MatrixType sum = 0;
for (unsigned int k = 0; k < m1.cols; k++)
{
sum += getMatrixAt(m1, r, k) * getMatrixAt(m2, k, c);
}
result.buffer[r * m2.cols + c] = sum;
}
}
return result;
}
+7
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@@ -6,6 +6,13 @@
typedef float MatrixType;
// TODO Matrixtyp definieren
typedef struct
{
MatrixType *buffer;
unsigned int rows;
unsigned int cols;
} Matrix;
Matrix createMatrix(unsigned int rows, unsigned int cols);
+1 -28
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@@ -71,32 +71,6 @@ void test_addFailsOnDifferentInputDimensions(void)
TEST_ASSERT_EQUAL_UINT32(0, result.cols);
}
void test_addSupportsBroadcasting(void)
{
MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
MatrixType buffer2[] = {7, 8};
Matrix matrix1 = {.rows=2, .cols=3, .buffer=buffer1};
Matrix matrix2 = {.rows=2, .cols=1, .buffer=buffer2};
Matrix result1 = add(matrix1, matrix2);
Matrix result2 = add(matrix2, matrix1);
float expectedResults[] = {8, 9, 10, 12, 13, 14};
TEST_ASSERT_EQUAL_UINT32(matrix1.rows, result1.rows);
TEST_ASSERT_EQUAL_UINT32(matrix1.cols, result1.cols);
TEST_ASSERT_EQUAL_UINT32(matrix1.rows, result2.rows);
TEST_ASSERT_EQUAL_UINT32(matrix1.cols, result2.cols);
TEST_ASSERT_EQUAL_INT(sizeof(expectedResults)/sizeof(expectedResults[0]), result1.rows * result1.cols);
TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, result1.buffer, result1.cols * result1.rows);
TEST_ASSERT_EQUAL_INT(sizeof(expectedResults)/sizeof(expectedResults[0]), result2.rows * result2.cols);
TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, result2.buffer, result2.cols * result2.rows);
free(result1.buffer);
free(result2.buffer);
}
void test_multiplyReturnsCorrectResults(void)
{
MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
@@ -164,7 +138,7 @@ void test_setMatrixAtFailsOnIndicesOutOfRange(void)
Matrix matrixToTest = {.rows=2, .cols=3, .buffer=buffer};
setMatrixAt(-1, matrixToTest, 2, 3);
TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, matrixToTest.buffer, sizeof(buffer)/sizeof(MatrixType));
TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, matrixToTest.buffer, matrixToTest.cols * matrixToTest.rows);
}
void setUp(void) {
@@ -185,7 +159,6 @@ int main()
RUN_TEST(test_clearMatrixSetsMembersToNull);
RUN_TEST(test_addReturnsCorrectResult);
RUN_TEST(test_addFailsOnDifferentInputDimensions);
RUN_TEST(test_addSupportsBroadcasting);
RUN_TEST(test_multiplyReturnsCorrectResults);
RUN_TEST(test_multiplyFailsOnWrongInputDimensions);
RUN_TEST(test_getMatrixAtReturnsCorrectResult);
@@ -8,7 +8,58 @@
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
{
// TODO
// TODO : Fehlerbehandlung
// Öffne die Datei zum Schreiben im Binärmodus
FILE *file = fopen(path, "wb");
if (!file) return;
// Schreibe den Datei-Tag
const char *tag = "__info2_neural_network_file_format__";
fwrite(tag, 1, strlen(tag), file);
// Schreibe die Anzahl der Layer
if (nn.numberOfLayers == 0) {
fclose(file);
return;
}
// Schreibe die Eingabe- und Ausgabegrößen des Netzwerks
int input = nn.layers[0].weights.cols;
int output = nn.layers[0].weights.rows;
fwrite(&input, sizeof(int), 1, file);
fwrite(&output, sizeof(int), 1, file);
// Schreibe die Layer-Daten
for (int i = 0; i < nn.numberOfLayers; i++)
{
const Layer *layer = &nn.layers[i];
int out = layer->weights.rows;
int in = layer->weights.cols;
fwrite(layer->weights.buffer, sizeof(MatrixType), out * in, file);
fwrite(layer->biases.buffer, sizeof(MatrixType), out * 1, file);
if (i + 1 < nn.numberOfLayers)
{
int nextOut = nn.layers[i + 1].weights.rows;
fwrite(&nextOut, sizeof(int), 1, file);
}
}
fclose(file);
// Debuging-Ausgabe
printf("prepareNeuralNetworkFile: Datei '%s' erstellt mit %u Layer(n)\n", path, nn.numberOfLayers);
for (unsigned int i = 0; i < nn.numberOfLayers; i++) {
Layer layer = nn.layers[i];
printf("Layer %u: weights (%u x %u), biases (%u x %u)\n",
i, layer.weights.rows, layer.weights.cols, layer.biases.rows, layer.biases.cols);
}
}
void test_loadModelReturnsCorrectNumberOfLayers(void)