Merge branch 'main' of https://git.efi.th-nuernberg.de/gitea/turtschinba100320/info2Praktikum-NeuronalesNetzBastiBjoern

.gitignore

@@ -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
+*.exe

Startcode/imageInput.c

@@ -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)
-{
-}

Startcode/matrix.c

@@ -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)
-{
-    
-}

imageInput.c

@@ -0,0 +1,99 @@
+#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,sizeof(unsigned short),1,datei);
+    fread(&width,sizeof(unsigned short),1,datei);
+    fread(&height,sizeof(unsigned short),1,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].width  = width;
+        series->images[i].height = height;
+        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;
+    
+}

imageInput.h

@@ -19,5 +19,5 @@ typedef struct
 
 GrayScaleImageSeries *readImages(const char *path);
 void clearSeries(GrayScaleImageSeries *series);
-
+FILE *checkFile(const char *path);
 #endif

imageInputTests.c

@@ -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,8, expectedHeight, 2, 1);
     series = readImages(path);
     TEST_ASSERT_NOT_NULL(series);
     TEST_ASSERT_NOT_NULL(series->images);

main.c

@@ -1,3 +1,4 @@
+
 #include <stdio.h>
 #include <stdlib.h>
 #include "imageInput.h"
@@ -6,6 +7,8 @@
 
 int main(int argc, char *argv[])
 {
+    //readImages("mnist_test.info2");
+    
     const unsigned int windowWidth = 800;
     const unsigned int windowHeight = 600;
 
@@ -29,7 +32,7 @@ int main(int argc, char *argv[])
                 unsigned char *predictions = NULL;
                 
                 printf("Processing %u images ...\n", series->count);
-
+                
                 predictions = predict(model, series->images, series->count);
 
                 if(predictions != NULL)
@@ -65,4 +68,5 @@ int main(int argc, char *argv[])
     }
 
     return exitCode;
-}
+    
+}

matrix.c

@@ -0,0 +1,128 @@
+#include <stdlib.h>
+#include <string.h>
+#include "matrix.h"
+#include <stdio.h>
+
+
+// Matrix erzeugen
+Matrix createMatrix(unsigned int rows, unsigned int cols)
+{
+    Matrix matrix;
+    matrix.buffer = NULL;
+    matrix.rows = 0;
+    matrix.cols = 0;
+
+    if (rows == 0 || cols == 0)
+        return matrix; // leere Matrix
+
+    matrix.buffer = (MatrixType *)malloc(rows * cols * sizeof(MatrixType));
+    if (!matrix.buffer)
+        return matrix; // Speicher konnte nicht reserviert werden
+
+    matrix.rows = rows;
+    matrix.cols = cols;
+
+    // Initialisiere alle Werte auf UNDEFINED_MATRIX_VALUE
+    for (unsigned int i = 0; i < rows * cols; i++)
+        matrix.buffer[i] = UNDEFINED_MATRIX_VALUE;
+
+    return matrix;
+}
+
+// Matrix Speicher freigeben
+void clearMatrix(Matrix *matrix)
+{
+    if (!matrix) return;
+
+    if (matrix->buffer)
+        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 (!matrix.buffer) return;
+    if (rowIdx >= matrix.rows || colIdx >= matrix.cols) return;
+
+    matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
+}
+
+// Wert auslesen
+MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
+{
+    if (!matrix.buffer) return UNDEFINED_MATRIX_VALUE;
+    if (rowIdx >= matrix.rows || colIdx >= matrix.cols) return UNDEFINED_MATRIX_VALUE;
+
+    return matrix.buffer[rowIdx * matrix.cols + colIdx];
+}
+// Matrizen addieren
+Matrix add(const Matrix m1, const Matrix m2)
+{
+    if (!m1.buffer || !m2.buffer) return createMatrix(0,0);
+
+    // gleiche Dimension
+    if (m1.rows == m2.rows && m1.cols == m2.cols)
+    {
+        Matrix result = createMatrix(m1.rows, m1.cols);
+        if (!result.buffer) return result;
+
+        for (unsigned int r = 0; r < m1.rows; r++)
+            for (unsigned int c = 0; c < m1.cols; c++)
+                result.buffer[r * result.cols + c] = m1.buffer[r * m1.cols + c] + m2.buffer[r * m2.cols + c];
+
+        return result;
+    }
+
+    // Matrix2 ist ein Spaltenvektor
+    if (m1.rows == m2.rows && m2.cols == 1)
+    {
+        Matrix result = createMatrix(m1.rows, m1.cols);
+        if (!result.buffer) return result;
+
+        for (unsigned int r = 0; r < m1.rows; r++)
+            for (unsigned int c = 0; c < m1.cols; c++)
+                result.buffer[r * result.cols + c] = m1.buffer[r * m1.cols + c] + m2.buffer[r];
+
+        return result;
+    }
+
+    // Matrix1 ist ein Spaltenvektor
+    if (m1.rows == m2.rows && m1.cols == 1)
+    {
+        Matrix result = createMatrix(m2.rows, m2.cols);
+        if (!result.buffer) return result;
+
+        for (unsigned int r = 0; r < m2.rows; r++)
+            for (unsigned int c = 0; c < m2.cols; c++)
+                result.buffer[r * result.cols + c] = m1.buffer[r] + m2.buffer[r * m2.cols + c];
+
+        return result;
+    }
+
+    // passt nicht
+    return createMatrix(0,0);
+}
+// Matrizen multiplizieren
+Matrix multiply(const Matrix m1, const Matrix m2)
+{
+    if (!m1.buffer || !m2.buffer) return createMatrix(0,0);
+    if (m1.cols != m2.rows) return createMatrix(0,0);
+
+    Matrix result = createMatrix(m1.rows, m2.cols);
+    if (!result.buffer) 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 += m1.buffer[r * m1.cols + k] * m2.buffer[k * m2.cols + c];
+            result.buffer[r * result.cols + c] = sum;
+        }
+
+    return result;
+}

matrix.h

@@ -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);

matrixTests.c

@@ -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);

neuralNetwork.c

@@ -197,7 +197,7 @@ static Matrix forward(const NeuralNetwork model, Matrix inputBatch)
 
     if(result.buffer != NULL)
     {
-        for(int i = 0; i < model.numberOfLayers; i++)
+                for(int i = 0; i < model.numberOfLayers; i++)
         {
             Matrix biasResult;
             Matrix weightResult;
@@ -246,7 +246,7 @@ unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
 {
     Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages);
     Matrix outputBatch = forward(model, inputBatch);
-
+    
     unsigned char *result = argmax(outputBatch);
     
     clearMatrix(&outputBatch);

neuralNetworkTests.c

@@ -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)