grafma98967/info2_pr_2_NeuronalesNetz
2
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Daten kopiert

Browse Source
This commit is contained in:
maxgrf 2025-11-24 12:14:10 +01:00
parent eb7ebe0a2b
commit 1fca7598d6
5 changed files with 273 additions and 52 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

82
imageInput.c
View File

@ -6,17 +6,95 @@
#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;
// Initialisiert einen Zeiger zur struct und reserviert Speicherplatz
GrayScaleImageSeries *series = malloc(sizeof(GrayScaleImageSeries));
if(series == NULL){
printf("Es ist nicht genügend Speicher übrig");
return NULL;
}
FILE * data = fopen(path, "rb");
if (data == NULL){
printf("Die Datei konnte nicht gelesen werden");
return NULL;
}
// Überprüfung, ob die Datei einen Header hat
char header[BUFFER_SIZE];
fread(header, strlen(FILE_HEADER_STRING), 1, data);
header[strlen(FILE_HEADER_STRING)] ='\0';
if(strncmp(header, FILE_HEADER_STRING, strlen(FILE_HEADER_STRING) )!= 0){
printf("Die Datei hat keinen Header");
fclose(data);
return NULL;
}
//liest die Anzahl der Bilder aus
fread(&series->count, sizeof(unsigned short),1, data);
series->images = malloc(series->count * sizeof(GrayScaleImage));
if (series->images == NULL){
printf("Es ist nicht genügend Speicher übrig");
fclose(data);
return NULL;
}
//liest die Höhe und Breite der Bilder aus
unsigned short height = 0, width = 0;
fread(&width, sizeof(unsigned short), 1, data);
fread(&height, sizeof(unsigned short), 1, data);
//reserviert Speicher für die Labels, die aber erst nach jedem Bild eingelesen werden
series->labels = malloc(sizeof(unsigned char) * series->count);
if (series->labels == NULL){
printf("Es ist nicht genügend Speicher übrig");
free(series->images);
fclose(data);
return NULL;
}
//liest jedes Bild einzeln aus und speichert es in images
for(int counter_picture = 0 ; counter_picture < series->count; counter_picture++){
// für jedes Bild muss vorher eine Größe festgelegt werden, die jedoch in diesem Fall immer gleich ist
series->images[counter_picture].width = width;
series->images[counter_picture].height =height;
unsigned int size_picture = height * width;
//reservieren des Speichers für Buffer, der die einzelnen Pixels speichert
series->images[counter_picture].buffer = malloc(size_picture* sizeof(GrayScalePixelType));
if (series->images[counter_picture].buffer == NULL){
printf("Es ist nicht genügend Speicher übrig");
free(series->images);
free(series);
fclose(data);
return NULL;
}
//einlesen der einzelnen Pixel in buffer
for(int counter_pixels = 0; counter_pixels < size_picture; counter_pixels++){
fread(&series->images[counter_picture].buffer[counter_pixels], sizeof(unsigned char), 1, data);
}
//einlesen der Labels
fread(&series->labels[counter_picture], sizeof(unsigned char), 1, data);
}
fclose(data);
return series;
}
// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
void clearSeries(GrayScaleImageSeries *series)
{
//erst den Speicherplatz der Pixel freigeben
for(int number= 0; number < series->count; number++){
free(series->images[number].buffer);
}
// dann die Bilder freigeben
free(series-> images);
free(series);
}

3
imageInputTests.c
View File

@ -123,7 +123,8 @@ void setUp(void) {
// Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
}
void tearDown(void) {
void tearDown(void)
{
// Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
}

101
matrix.c
View File

@ -6,30 +6,119 @@
Matrix createMatrix(unsigned int rows, unsigned int cols)
{
Matrix matrix = {NULL, 0, 0};
if (rows == 0 || cols == 0)
return matrix; //gibt leere Matrix zurück
matrix.buffer = (MatrixType *)calloc(rows * cols, sizeof(MatrixType));
if (matrix.buffer == NULL) //auf verfügbaren Speicherplatz prüfen
return matrix;
matrix.rows = rows;
matrix.cols = cols;
return matrix; //Matrix zurückgeben
}
void clearMatrix(Matrix *matrix)
{
if (matrix != NULL)
{
free(matrix->buffer); //Speicherplatz bereinigen
matrix->buffer = NULL; //Werte auf 0 setzen
matrix->rows = 0;
matrix->cols = 0;
}
}
void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
{
if (rowIdx < matrix.rows && colIdx < matrix.cols && matrix.buffer != NULL) //Prüft ob Zugriff möglich
matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
//schreibt 2D element in 1D Liste: Element_Reihe*Matrix_Spalten + Element_Spalte
}
MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
{
if (rowIdx >= matrix.rows || colIdx >= matrix.cols || matrix.buffer == NULL)
return 0; // Sicherheitscheck
return matrix.buffer[rowIdx * matrix.cols + colIdx];
}
// TODO: Funktionen implementieren
Matrix add(const Matrix matrix1, const Matrix matrix2)
{
// immer Probe, gleiche Zeilen der Matrizen
// "Elementweise Addition": Probe, ob matrix gleiche größe hat
if (matrix1.rows == matrix2.rows && matrix1.cols == matrix2.cols)
{
Matrix result_add = createMatrix(matrix1.rows, matrix1.cols);
for (int r = 0; r < matrix1.rows; r++)
{
for (int c = 0; c < matrix1.cols; c++)
{
// first version: matrix_add[r][c] = matrix1[r][c] + matrix2[r][c]
MatrixType sum = getMatrixAt(matrix1, r, c) + getMatrixAt(matrix2, r, c);
setMatrixAt(sum, result_add, r, c);
}
}
return result_add;
}
// "Broadcasting": matrix1 hat 1 Spalte
if (matrix1.rows == matrix2.rows && matrix1.cols == 1)
{
Matrix result_add = createMatrix(matrix1.rows, matrix2.cols);
for (int r = 0; r < matrix1.rows; r++)
{
for (int c = 0; c < matrix2.cols; c++)
{
MatrixType sum = getMatrixAt(matrix2, r, c) + getMatrixAt(matrix1, r, 0);
setMatrixAt(sum, result_add, r, c);
}
}
return result_add;
}
// "Broadcasting": matrix2 hat 1 Spalte
if (matrix1.rows == matrix2.rows && matrix2.cols == 1)
{
Matrix result_add = createMatrix(matrix1.rows, matrix1.cols);
for (int r = 0; r < matrix1.rows; r++)
{
for (int c = 0; c < matrix1.cols; c++)
{
MatrixType sum = getMatrixAt(matrix1, r, c) + getMatrixAt(matrix2, r, 0);
setMatrixAt(sum, result_add, r, c);
}
}
return result_add;
}
return createMatrix(0, 0);
}
Matrix multiply(const Matrix matrix1, const Matrix matrix2)
{
MatrixType buffer_add;
if (!matrix1.buffer || !matrix2.buffer) // Probe ob leere Matrize vorliegt
return createMatrix(0, 0);
if (matrix1.cols != matrix2.rows) // Probe ob Spalten1 = Zeilen2
return createMatrix(0, 0);
Matrix result_mul = createMatrix(matrix1.rows, matrix2.cols);
for (unsigned int index = 0; index < matrix1.rows; index++)
{
for (unsigned int shift = 0; shift < matrix2.cols; shift++)
{
buffer_add = 0;
for (unsigned int skalar = 0; skalar < matrix1.cols; skalar++)
{
buffer_add += getMatrixAt(matrix1, index, skalar) * getMatrixAt(matrix2, skalar, shift);
}
setMatrixAt(buffer_add, result_mul, index, shift);
}
}
return result_mul;
}

5
matrix.h
View File

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

134
neuralNetworkTests.c
View File

@ -5,10 +5,56 @@
#include "unity.h"
#include "neuralNetwork.h"
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
{
// TODO
FILE *file = fopen(path, "wb");
if (!file)
return;
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)
@ -16,15 +62,15 @@ 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};
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}};
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 expectedNet = {.layers = layers, .numberOfLayers = 2};
NeuralNetwork netUnderTest;
prepareNeuralNetworkFile(path, expectedNet);
@ -40,12 +86,12 @@ 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};
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}};
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
Layer layers[] = {{.weights = weights, .biases = biases}};
NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
NeuralNetwork netUnderTest;
prepareNeuralNetworkFile(path, expectedNet);
@ -63,12 +109,12 @@ 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};
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}};
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
Layer layers[] = {{.weights = weights, .biases = biases}};
NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
NeuralNetwork netUnderTest;
prepareNeuralNetworkFile(path, expectedNet);
@ -86,12 +132,12 @@ 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};
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}};
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
Layer layers[] = {{.weights = weights, .biases = biases}};
NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
NeuralNetwork netUnderTest;
prepareNeuralNetworkFile(path, expectedNet);
@ -111,12 +157,12 @@ 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};
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}};
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
Layer layers[] = {{.weights = weights, .biases = biases}};
NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
NeuralNetwork netUnderTest;
prepareNeuralNetworkFile(path, expectedNet);
@ -138,7 +184,7 @@ void test_loadModelFailsOnWrongFileTag(void)
NeuralNetwork netUnderTest;
FILE *file = fopen(path, "wb");
if(file != NULL)
if (file != NULL)
{
const char *fileTag = "info2_neural_network_file_format";
@ -159,12 +205,12 @@ 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};
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}};
Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1};
Layer layers[] = {{.weights = weights, .biases = biases}};
NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1};
NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1};
NeuralNetwork netUnderTest;
prepareNeuralNetworkFile(path, expectedNet);
@ -181,7 +227,7 @@ void test_clearModelSetsMembersToNull(void)
static void someActivation(Matrix *matrix)
{
for(int i = 0; i < matrix->rows * matrix->cols; i++)
for (int i = 0; i < matrix->rows * matrix->cols; i++)
{
matrix->buffer[i] = fabs(matrix->buffer[i]);
}
@ -192,23 +238,23 @@ void test_predictReturnsCorrectLabels(void)
const unsigned char expectedLabels[] = {4, 2};
GrayScalePixelType imageBuffer1[] = {10, 30, 25, 17};
GrayScalePixelType imageBuffer2[] = {20, 40, 10, 128};
GrayScaleImage inputImages[] = {{.buffer=imageBuffer1, .width=2, .height=2}, {.buffer=imageBuffer2, .width=2, .height=2}};
GrayScaleImage inputImages[] = {{.buffer = imageBuffer1, .width = 2, .height = 2}, {.buffer = imageBuffer2, .width = 2, .height = 2}};
MatrixType weightsBuffer1[] = {1, -2, 3, -4, 5, -6, 7, -8};
MatrixType weightsBuffer2[] = {-9, 10, 11, 12, 13, 14};
MatrixType weightsBuffer3[] = {-15, 16, 17, 18, -19, 20, 21, 22, 23, -24, 25, 26, 27, -28, -29};
Matrix weights1 = {.buffer=weightsBuffer1, .rows=2, .cols=4};
Matrix weights2 = {.buffer=weightsBuffer2, .rows=3, .cols=2};
Matrix weights3 = {.buffer=weightsBuffer3, .rows=5, .cols=3};
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};
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]));
@ -216,11 +262,13 @@ void test_predictReturnsCorrectLabels(void)
free(predictedLabels);
}
void setUp(void) {
void setUp(void)
{
// Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
}
void tearDown(void) {
void tearDown(void)
{
// Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
}