initiate Neuronales_Netz

Neuronales_Netz/.vscode/c_cpp_properties.json

@@ -0,0 +1,18 @@
+{
+  "configurations": [
+    {
+      "name": "linux-gcc-x64",
+      "includePath": [
+        "${workspaceFolder}/**"
+      ],
+      "compilerPath": "/usr/bin/gcc",
+      "cStandard": "${default}",
+      "cppStandard": "${default}",
+      "intelliSenseMode": "linux-gcc-x64",
+      "compilerArgs": [
+        ""
+      ]
+    }
+  ],
+  "version": 4
+}

Neuronales_Netz/.vscode/launch.json

@@ -0,0 +1,24 @@
+{
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "C/C++ Runner: Debug Session",
+      "type": "cppdbg",
+      "request": "launch",
+      "args": [],
+      "stopAtEntry": false,
+      "externalConsole": false,
+      "cwd": "/home/tommy/Desktop/Studium/3.Semester/I2-Praktikum/Playground/Neuronales_Netz",
+      "program": "/home/tommy/Desktop/Studium/3.Semester/I2-Praktikum/Playground/Neuronales_Netz/build/Debug/outDebug",
+      "MIMode": "gdb",
+      "miDebuggerPath": "gdb",
+      "setupCommands": [
+        {
+          "description": "Enable pretty-printing for gdb",
+          "text": "-enable-pretty-printing",
+          "ignoreFailures": true
+        }
+      ]
+    }
+  ]
+}

Neuronales_Netz/.vscode/settings.json

@@ -0,0 +1,59 @@
+{
+  "C_Cpp_Runner.cCompilerPath": "gcc",
+  "C_Cpp_Runner.cppCompilerPath": "g++",
+  "C_Cpp_Runner.debuggerPath": "gdb",
+  "C_Cpp_Runner.cStandard": "",
+  "C_Cpp_Runner.cppStandard": "",
+  "C_Cpp_Runner.msvcBatchPath": "",
+  "C_Cpp_Runner.useMsvc": false,
+  "C_Cpp_Runner.warnings": [
+    "-Wall",
+    "-Wextra",
+    "-Wpedantic",
+    "-Wshadow",
+    "-Wformat=2",
+    "-Wcast-align",
+    "-Wconversion",
+    "-Wsign-conversion",
+    "-Wnull-dereference"
+  ],
+  "C_Cpp_Runner.msvcWarnings": [
+    "/W4",
+    "/permissive-",
+    "/w14242",
+    "/w14287",
+    "/w14296",
+    "/w14311",
+    "/w14826",
+    "/w44062",
+    "/w44242",
+    "/w14905",
+    "/w14906",
+    "/w14263",
+    "/w44265",
+    "/w14928"
+  ],
+  "C_Cpp_Runner.enableWarnings": true,
+  "C_Cpp_Runner.warningsAsError": false,
+  "C_Cpp_Runner.compilerArgs": [],
+  "C_Cpp_Runner.linkerArgs": [],
+  "C_Cpp_Runner.includePaths": [],
+  "C_Cpp_Runner.includeSearch": [
+    "*",
+    "**/*"
+  ],
+  "C_Cpp_Runner.excludeSearch": [
+    "**/build",
+    "**/build/**",
+    "**/.*",
+    "**/.*/**",
+    "**/.vscode",
+    "**/.vscode/**"
+  ],
+  "C_Cpp_Runner.useAddressSanitizer": false,
+  "C_Cpp_Runner.useUndefinedSanitizer": false,
+  "C_Cpp_Runner.useLeakSanitizer": false,
+  "C_Cpp_Runner.showCompilationTime": false,
+  "C_Cpp_Runner.useLinkTimeOptimization": false,
+  "C_Cpp_Runner.msvcSecureNoWarnings": false
+}

Neuronales_Netz/imageInput.c

@@ -6,17 +6,157 @@
 #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;
+    FILE* file = fopen(path, "rb");
+    
+    if(file == NULL) 
+    {
+        puts("could not open file.");
+
+        return NULL;
+    }
+
+    if(!checkFileHeaderString(file))
+    {
+        puts("wrong File.");
+
+        return NULL;
+    }
+
+    unsigned short count = getCount(file);
+    if(!count)
+    {
+        puts("could not read number of images.");
+
+        return NULL;
+    }
+
+    unsigned short width = getWidth(file);
+    if(!width)
+    {
+        puts("could not read Width of images.");
+
+        return NULL;
+    }
+
+    unsigned short height = getHeight(file);
+    if(!height)
+    {
+        puts("could not read height of images.");
+
+        return NULL;
+    }
+
+    GrayScaleImageSeries *series = createSeries(count);
+
+    series->count = count;
+    unsigned short resolution = width * height;
+
+    for(unsigned int i = 0; i < series->count; i++)
+    {
+        series->images[i].width = width;
+        series->images[i].height = height;
+
+        series->images[i].buffer = getPixels(file, resolution);
+        series->labels[i] = getLabel(file);
+    }
+
+    fclose(file);
     
     return series;
 }
 
+
+
 // TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
 void clearSeries(GrayScaleImageSeries *series)
 {   
+    for(int i = 0; i < series->count; i++)
+    {
+        free(series->images[i].buffer);
+    }
+
+    free(series->images);
+    free(series->labels);
+    free(series);
+}
+
+
+int checkFileHeaderString(FILE *file)
+{
+    char buffer[strlen(FILE_HEADER_STRING)];
+
+    fread(buffer, sizeof(char), strlen(FILE_HEADER_STRING), file);
+
+    for(int i = 0; i < strlen(FILE_HEADER_STRING); i++)
+    {
+        if(buffer[i] != FILE_HEADER_STRING[i])
+        {
+            return 0;
+        }
+    }
+    return 1;
+}
+
+unsigned short getCount(FILE *file)
+{
+    unsigned short count = 0;
+
+    fread(&count, sizeof(unsigned short), 1, file);
+
+    return count;
+}
+
+unsigned short getWidth(FILE *file)
+{
+    unsigned short width = 0;
+
+    fread(&width, sizeof(unsigned short), 1, file);
+
+    return width;
+}
+
+unsigned short getHeight(FILE *file)
+{
+    unsigned short height = 0;
+
+    fread(&height, sizeof(unsigned short), 1, file);
+
+    return height;
+}
+
+GrayScaleImageSeries *createSeries(unsigned short count)
+{
+    GrayScaleImage *images = (GrayScaleImage*) calloc(count, sizeof(GrayScaleImage));
+    unsigned char *labels = (unsigned char*) calloc(count, sizeof(unsigned char));
+
+    GrayScaleImageSeries *series = (GrayScaleImageSeries*) calloc(1, sizeof(GrayScaleImageSeries));
+    series->images = images;
+    series->labels = labels;
+
+    return series;
+}
+
+GrayScalePixelType *getPixels(FILE *file, unsigned short resolution)
+{
+    GrayScalePixelType *pixels = (GrayScalePixelType*) calloc(resolution, sizeof(GrayScalePixelType));
+
+    fread(pixels, sizeof(GrayScalePixelType), resolution, file);
+
+    return pixels;
+}
+
+unsigned char getLabel(FILE *file)
+{
+    unsigned char label = '\0';
+
+    fread(&label, sizeof(unsigned char), 1, file);
+
+    return label;
 }

Neuronales_Netz/imageInput.h

@@ -19,5 +19,12 @@ typedef struct
 
 GrayScaleImageSeries *readImages(const char *path);
 void clearSeries(GrayScaleImageSeries *series);
+int checkFileHeaderString(FILE *file);
+unsigned short getCount(FILE *file);
+unsigned short getWidth(FILE *file);
+unsigned short getHeight(FILE *file);
+GrayScaleImageSeries *createSeries(unsigned short count);
+GrayScalePixelType *getPixels(FILE *file, unsigned short resolution);
+unsigned char getLabel(FILE *file);
 
 #endif

Neuronales_Netz/imageInputTests.c

@@ -119,6 +119,26 @@ void test_readImagesFailsOnWrongFileTag(void)
     remove(path);
 }
 
+void test_readImagesReturnsCorrectPixels(void)
+{
+    GrayScalePixelType expectedResult = 0;
+
+    GrayScaleImageSeries *series = NULL;
+    const char *path = "testFile.info2";
+    prepareImageFile(path, 8, 8, 2, 0);
+    series = readImages(path);
+
+    for (int i = 0; i < 2; i++) 
+    {
+        for(int j = 0; j < 8 * 8; j++)
+        {
+            TEST_ASSERT_EQUAL_UINT8(expectedResult, series->images[i].buffer[j]);
+        }
+    }
+    clearSeries(series);
+    remove(path);
+}
+
 void setUp(void) {
     // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
 }
@@ -138,6 +158,8 @@ int main()
     RUN_TEST(test_readImagesReturnsCorrectLabels);
     RUN_TEST(test_readImagesReturnsNullOnNotExistingPath);
     RUN_TEST(test_readImagesFailsOnWrongFileTag);
+    RUN_TEST(test_readImagesReturnsCorrectPixels);
+
 
     return UNITY_END();
 }

Neuronales_Netz/matrix.c

@@ -3,33 +3,135 @@
 #include "matrix.h"
 
 // TODO Matrix-Funktionen implementieren
+// typedef struct
+// {
+//     unsigned int *buffer;
+//     unsigned int rows;
+//     unsigned int cols;
+// } Matrix;
+
 
 Matrix createMatrix(unsigned int rows, unsigned int cols)
 {
+    if(rows == 0 || cols == 0)
+    {
+        Matrix noMatrix = {NULL, 0, 0};
 
+        return noMatrix;
+    }
+
+    Matrix matrix = {(MatrixType *)calloc(rows * cols, sizeof(int)) ,rows, cols};
+
+    return matrix;
 }
 
 void clearMatrix(Matrix *matrix)
 {
-    
+    free(matrix->buffer);
+    matrix->rows = 0;
+    matrix->cols = 0;
+    matrix->buffer = NULL;
 }
 
 void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
+    if(rowIdx >= matrix.rows || colIdx >= matrix.cols)
+    {
+        return;
+    }
 
+    matrix.buffer[matrix.cols * rowIdx + colIdx] = value;
 }
 
 MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
 {
+    if(rowIdx >= matrix.rows || colIdx >= matrix.cols)
+    {
+        return UNDEFINED_MATRIX_VALUE;
+    }
 
+    MatrixType matrixAt = matrix.buffer[matrix.cols * rowIdx + colIdx];
+
+    return matrixAt;
 }
 
 Matrix add(const Matrix matrix1, const Matrix matrix2)
 {
+    if(matrix1.rows == matrix2.rows && matrix1.cols == matrix2.cols)
+    {
+        Matrix matrixSum = createMatrix(matrix1.rows, matrix1.cols);
 
+        for(int i = 0; i < matrix1.rows; i++)
+        {
+            for(int j = 0; j < matrix1.cols; j++)
+            {
+                MatrixType value = getMatrixAt(matrix1, i, j) + getMatrixAt(matrix2, i, j);
+
+                setMatrixAt(value, matrixSum, i, j);
+            }
+
+        }
+        return matrixSum;
+    }
+
+    if((matrix1.cols == 1 || matrix2.cols == 1) && matrix1.rows == matrix2.rows)
+    {
+        if(matrix1.cols == 1)
+        {
+            return broadcasting(matrix2, matrix1);
+        }
+        else
+        {
+            return broadcasting(matrix1, matrix2);
+        }
+    }
+
+    Matrix failedMatrix = createMatrix(0, 0);
+
+    return failedMatrix;
+}
+
+Matrix broadcasting(const Matrix matrix, const Matrix vector)
+{
+    Matrix matrixSum = createMatrix(matrix.rows, matrix.cols);
+
+    for(int i = 0; i < vector.rows; i++)
+    {
+        for(int j = 0; j < matrix.cols; j++)
+        {
+            MatrixType value = getMatrixAt(matrix, i, j) + getMatrixAt(vector, i, 0);
+
+            setMatrixAt(value, matrixSum, i, j);
+        }
+    }
+    return matrixSum;
 }
 
 Matrix multiply(const Matrix matrix1, const Matrix matrix2)
 {
+    if(matrix1.cols != matrix2.rows)
+    {
+        Matrix failedMatrix = createMatrix(0, 0);
 
+        return failedMatrix;
+    }
+
+    Matrix matrixRes = createMatrix(matrix1.rows, matrix2.cols);
+
+    for(int i = 0; i < matrixRes.rows; i++)
+    {
+        for(int j = 0; j < matrixRes.cols; j++)
+        {
+            MatrixType sum = 0;
+
+            for(int k = 0; k < matrix1.cols; k++)
+            {
+                MatrixType product = getMatrixAt(matrix1, i, k) * getMatrixAt(matrix2, k, j);
+
+                sum += product; 
+            }
+            setMatrixAt(sum, matrixRes, i, j);
+        }
+    }
+    return matrixRes;
 }

Neuronales_Netz/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);
@@ -13,6 +20,7 @@ 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 broadcasting(const Matrix matrix, const Matrix vector);
 Matrix multiply(const Matrix matrix1, const Matrix matrix2);
 
 

Neuronales_Netz/neuralNetwork.c

@@ -80,7 +80,6 @@ static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols)
         if(fread(matrix.buffer, sizeof(MatrixType), rows*cols, file) != rows*cols)
             clearMatrix(&matrix);
     }
-
     return matrix;
 }
 
@@ -146,13 +145,16 @@ NeuralNetwork loadModel(const char *path)
                 layerBuffer = (Layer *)realloc(model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
 
                 if(layerBuffer != NULL)
+                {
                     model.layers = layerBuffer;
+                }
                 else
                 {
                     clearModel(&model);
                     break;
                 }
 
+                
                 model.layers[model.numberOfLayers] = layer;
                 model.numberOfLayers++;
 

Neuronales_Netz/neuralNetworkTests.c

@@ -9,6 +9,31 @@
 static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
 {
     // TODO
+    FILE *file = fopen(path, "wb");
+
+    if(file != NULL)
+    {
+        const char *fileTag = "__info2_neural_network_file_format__";
+        fwrite(fileTag, sizeof(fileTag[0]), strlen(fileTag), file);
+        fwrite(&nn.layers[0].weights.cols, sizeof(int), 1, file);
+        fwrite(&nn.layers[0].weights.rows, sizeof(int), 1, file);
+
+        for(int i = 0; i < nn.numberOfLayers; i++)
+        {
+            unsigned int sizeWeights = nn.layers[i].weights.rows * nn.layers[i].weights.cols;
+            unsigned int sizeBiases = nn.layers[i].biases.rows;
+
+            fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), sizeWeights, file);
+            fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), sizeBiases, file);
+
+            if(i < nn.numberOfLayers - 1)
+            {
+                fwrite(&nn.layers[i].weights.cols, sizeof(unsigned int), 1, file);
+            }
+        }
+
+        fclose(file);
+    }
 }
 
 void test_loadModelReturnsCorrectNumberOfLayers(void)