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

imageInput.c

@@ -5,18 +5,95 @@
 
 #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

@@ -54,7 +54,7 @@ void test_readImagesReturnsCorrectImageWidth(void)
     GrayScaleImageSeries *series = NULL;
     const unsigned short expectedWidth = 10;
     const char *path = "testFile.info2";
-    prepareImageFile(path, 8, expectedWidth, 2, 1);
+    prepareImageFile(path, expectedWidth, 8, 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, expectedHeight, 8, 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

@@ -1,35 +1,128 @@
 #include <stdlib.h>
 #include <string.h>
 #include "matrix.h"
+#include <stdio.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;
+
+    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;
 
-Matrix add(const Matrix matrix1, const Matrix matrix2)
-{
-    
+    return matrix.buffer[rowIdx * matrix.cols + colIdx];
 }
-
-Matrix multiply(const Matrix matrix1, const Matrix matrix2)
+// 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);

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)