NeuralNetzworkTests Fertig

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "files.associations": {
+        "unity.h": "c"
+    }
+}

imageInput.h

@@ -12,7 +12,7 @@ typedef struct
 
 typedef struct
 {
-    GrayScaleImage *images; //in sich verschachtelte Struktur
+    GrayScaleImage *images;
     unsigned char *labels;
     unsigned int count;
 } GrayScaleImageSeries;

matrix.c

@@ -2,20 +2,22 @@
 #include <string.h>
 #include "matrix.h"
 
+// TODO Matrix-Funktionen implementieren
+
 Matrix createMatrix(unsigned int rows, unsigned int cols)
 {
-    Matrix m = {NULL, 0, 0};
+    Matrix matrix = {NULL, 0, 0};
 
     if (rows == 0 || cols == 0)
-        return m;
+        return matrix;   //gibt leere Matrix zurück
 
-    m.buffer = (MatrixType *)calloc(rows * cols, sizeof(MatrixType));
-    if (m.buffer == NULL)
-        return m;
+    matrix.buffer = (MatrixType *)calloc(rows * cols, sizeof(MatrixType));
+    if (matrix.buffer == NULL) //auf verfügbaren Speicherplatz prüfen
+        return matrix;
 
-    m.rows = rows;
-    m.cols = cols;
-    return m;
+    matrix.rows = rows;  
+    matrix.cols = cols;
+    return matrix;   //Matrix zurückgeben
 }
 
 void clearMatrix(Matrix *matrix)
@@ -23,18 +25,18 @@ void clearMatrix(Matrix *matrix)
 
     if (matrix != NULL)
     {
-        free(matrix->buffer);
-        matrix->buffer = 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)
-// Matrix matrix zu Matrix *matrix, empfehlung
+void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx) 
 {
-    if (rowIdx < matrix.rows && colIdx < matrix.cols && matrix.buffer != NULL)
-        matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
+    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)
@@ -45,11 +47,10 @@ MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int co
 }
 
 // TODO: Funktionen implementieren
-
 Matrix add(const Matrix matrix1, const Matrix matrix2)
 {
-    //  check, equal rows
-    // "Elementweise Addition": test, if two matrix has exact size
+    //  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);
@@ -64,7 +65,7 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
         }
         return result_add;
     }
-    // "Broadcasting": matrix1 has 1 collum
+    // "Broadcasting": matrix1 hat 1 Spalte
     if (matrix1.rows == matrix2.rows && matrix1.cols == 1)
     {
         Matrix result_add = createMatrix(matrix1.rows, matrix2.cols);
@@ -78,7 +79,7 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
         }
         return result_add;
     }
-    // "Broadcasting": matrix2 has 1 collum
+    // "Broadcasting": matrix2 hat 1 Spalte
     if (matrix1.rows == matrix2.rows && matrix2.cols == 1)
     {
         Matrix result_add = createMatrix(matrix1.rows, matrix1.cols);
@@ -98,13 +99,14 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
 
 Matrix multiply(const Matrix matrix1, const Matrix matrix2)
 {
-    // Needed: rows/Zeilen, collums/Spalten
     MatrixType buffer_add;
-    // Probe ob Spalten1 = Zeilen2
-    if (matrix1.cols != matrix2.rows)
+    
+    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); // ""
+    Matrix result_mul = createMatrix(matrix1.rows, matrix2.cols);
 
     for (unsigned int index = 0; index < matrix1.rows; index++)
     {
@@ -113,12 +115,10 @@ Matrix multiply(const Matrix matrix1, const Matrix matrix2)
             buffer_add = 0;
             for (unsigned int skalar = 0; skalar < matrix1.cols; skalar++)
             {
-                // buffer_add += matrix1[index][skalar]*matrix2[skalar][shift];
                 buffer_add += getMatrixAt(matrix1, index, skalar) * getMatrixAt(matrix2, skalar, shift);
             }
-            // matrix_mul[index][shift] = buffer_add;
-            setMatrixAt(buffer_add, result_mul, index, shift); // result als Pointer, also mit &result
+            setMatrixAt(buffer_add, result_mul, index, shift);
         }
     }
     return result_mul;
-}
+}

matrix.h

@@ -7,10 +7,10 @@ typedef float MatrixType;
 
 // TODO Matrixtyp definieren
 typedef struct {
-    MatrixType *matrix;
+    MatrixType *buffer;
     unsigned int rows;
     unsigned int cols;
-}Matrix;
+} Matrix;
 
 
 Matrix createMatrix(unsigned int rows, unsigned int cols);

neuralNetworkTests.c

@@ -8,7 +8,57 @@
 
 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)