From 56d59b1b5049f20c0f75912c44eed9359483a3a2 Mon Sep 17 00:00:00 2001 From: Kristin Date: Sun, 23 Nov 2025 16:41:57 +0100 Subject: [PATCH] neuralNetworkTests --- matrix.c | 29 ++- neuralNetwork.c | 435 +++++++++++++++++-------------------- neuralNetworkTests.c | 498 ++++++++++++++++++++++++------------------- 3 files changed, 511 insertions(+), 451 deletions(-) diff --git a/matrix.c b/matrix.c index 7d0a649..ded4faa 100644 --- a/matrix.c +++ b/matrix.c @@ -218,4 +218,31 @@ Matrix add(const Matrix matrix1, const Matrix matrix2) { return error; } } -Matrix multiply(const Matrix matrix1, const Matrix matrix2) { return matrix1; } + +Matrix multiply(const Matrix matrix1, const Matrix matrix2) { + // Spalten1 müssen gleich zeilen2 sein! dann multiplizieren + if (matrix1.cols == matrix2.rows) { + Matrix multMatrix = createMatrix(matrix1.rows, matrix2.cols); + // durch neue matrix iterieren + for (int r = 0; r < matrix1.rows; r++) { + for (int c = 0; c < matrix2.cols; c++) { + MatrixType sum = 0.0; + // skalarprodukte berechnen, k damit die ganze zeile mal die ganze + // spalte genommen wird quasi + for (int k = 0; k < matrix1.cols; k++) { + // sum+= + // matrix1.buffer[r*matrix1.cols+k]*matrix2.buffer[k*matrix2.cols+c]; + sum += getMatrixAt(matrix1, r, k) * getMatrixAt(matrix2, k, c); + } + // Ergebnisse in neue matrix speichern + setMatrixAt(sum, multMatrix, r, c); + } + } + return multMatrix; + } + // sonst fehler, kein multiply möglich + else { + Matrix errorMatrix = {0, 0, NULL}; + return errorMatrix; + } +} \ No newline at end of file diff --git a/neuralNetwork.c b/neuralNetwork.c index bd8f164..7697dd2 100644 --- a/neuralNetwork.c +++ b/neuralNetwork.c @@ -1,268 +1,235 @@ -#include -#include -#include -#include #include "neuralNetwork.h" +#include +#include +#include +#include #define BUFFER_SIZE 100 #define FILE_HEADER_STRING "__info2_neural_network_file_format__" -static void softmax(Matrix *matrix) -{ - if(matrix->cols > 0) - { - double *colSums = (double *)calloc(matrix->cols, sizeof(double)); +static void softmax(Matrix *matrix) { + if (matrix->cols > 0) { + double *colSums = (double *)calloc(matrix->cols, sizeof(double)); - if(colSums != NULL) - { - for(int colIdx = 0; colIdx < matrix->cols; colIdx++) - { - for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) - { - MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx)); - setMatrixAt(expValue, *matrix, rowIdx, colIdx); - colSums[colIdx] += expValue; - } - } - - for(int colIdx = 0; colIdx < matrix->cols; colIdx++) - { - for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) - { - MatrixType normalizedValue = getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx]; - setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx); - } - } - free(colSums); + if (colSums != NULL) { + for (int colIdx = 0; colIdx < matrix->cols; colIdx++) { + for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) { + MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx)); + setMatrixAt(expValue, *matrix, rowIdx, colIdx); + colSums[colIdx] += expValue; } - } -} + } -static void relu(Matrix *matrix) -{ - for(int i = 0; i < matrix->rows * matrix->cols; i++) - { - matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0; - } -} - -static int checkFileHeader(FILE *file) -{ - int isValid = 0; - int fileHeaderLen = strlen(FILE_HEADER_STRING); - char buffer[BUFFER_SIZE] = {0}; - - if(BUFFER_SIZE-1 < fileHeaderLen) - fileHeaderLen = BUFFER_SIZE-1; - - if(fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen) - isValid = strcmp(buffer, FILE_HEADER_STRING) == 0; - - return isValid; -} - -static unsigned int readDimension(FILE *file) -{ - int dimension = 0; - - if(fread(&dimension, sizeof(int), 1, file) != 1) - dimension = 0; - - return dimension; -} - -static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols) -{ - Matrix matrix = createMatrix(rows, cols); - - if(matrix.buffer != NULL) - { - if(fread(matrix.buffer, sizeof(MatrixType), rows*cols, file) != rows*cols) - clearMatrix(&matrix); - } - - return matrix; -} - -static Layer readLayer(FILE *file, unsigned int inputDimension, unsigned int outputDimension) -{ - Layer layer; - layer.weights = readMatrix(file, outputDimension, inputDimension); - layer.biases = readMatrix(file, outputDimension, 1); - - return layer; -} - -static int isEmptyLayer(const Layer layer) -{ - return layer.biases.cols == 0 || layer.biases.rows == 0 || layer.biases.buffer == NULL || layer.weights.rows == 0 || layer.weights.cols == 0 || layer.weights.buffer == NULL; -} - -static void clearLayer(Layer *layer) -{ - if(layer != NULL) - { - clearMatrix(&layer->weights); - clearMatrix(&layer->biases); - layer->activation = NULL; - } -} - -static void assignActivations(NeuralNetwork model) -{ - for(int i = 0; i < (int)model.numberOfLayers-1; i++) - { - model.layers[i].activation = relu; - } - - if(model.numberOfLayers > 0) - model.layers[model.numberOfLayers-1].activation = softmax; -} - -NeuralNetwork loadModel(const char *path) -{ - NeuralNetwork model = {NULL, 0}; - FILE *file = fopen(path, "rb"); - - if(file != NULL) - { - if(checkFileHeader(file)) - { - unsigned int inputDimension = readDimension(file); - unsigned int outputDimension = readDimension(file); - - while(inputDimension > 0 && outputDimension > 0) - { - Layer layer = readLayer(file, inputDimension, outputDimension); - Layer *layerBuffer = NULL; - - if(isEmptyLayer(layer)) - { - clearLayer(&layer); - clearModel(&model); - break; - } - - 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++; - - inputDimension = outputDimension; - outputDimension = readDimension(file); - } + for (int colIdx = 0; colIdx < matrix->cols; colIdx++) { + for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) { + MatrixType normalizedValue = + getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx]; + setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx); } - fclose(file); - - assignActivations(model); + } + free(colSums); } - - return model; + } } -static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count) -{ - Matrix matrix = {NULL, 0, 0}; +static void relu(Matrix *matrix) { + for (int i = 0; i < matrix->rows * matrix->cols; i++) { + matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0; + } +} - if(count > 0 && images != NULL) - { - matrix = createMatrix(images[0].height * images[0].width, count); +static int checkFileHeader(FILE *file) { + int isValid = 0; + int fileHeaderLen = strlen(FILE_HEADER_STRING); + char buffer[BUFFER_SIZE] = {0}; - if(matrix.buffer != NULL) - { - for(int i = 0; i < count; i++) - { - for(int j = 0; j < images[i].width * images[i].height; j++) - { - setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i); - } - } + if (BUFFER_SIZE - 1 < fileHeaderLen) + fileHeaderLen = BUFFER_SIZE - 1; + + if (fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen) + isValid = strcmp(buffer, FILE_HEADER_STRING) == 0; + + return isValid; +} + +static unsigned int readDimension(FILE *file) { + int dimension = 0; + + if (fread(&dimension, sizeof(int), 1, file) != 1) + dimension = 0; + + return dimension; +} + +static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols) { + Matrix matrix = createMatrix(rows, cols); + + if (matrix.buffer != NULL) { + if (fread(matrix.buffer, sizeof(MatrixType), rows * cols, file) != + rows * cols) + clearMatrix(&matrix); + } + + return matrix; +} + +static Layer readLayer(FILE *file, unsigned int inputDimension, + unsigned int outputDimension) { + Layer layer; + layer.weights = readMatrix(file, outputDimension, inputDimension); + layer.biases = readMatrix(file, outputDimension, 1); + + return layer; +} + +static int isEmptyLayer(const Layer layer) { + return layer.biases.cols == 0 || layer.biases.rows == 0 || + layer.biases.buffer == NULL || layer.weights.rows == 0 || + layer.weights.cols == 0 || layer.weights.buffer == NULL; +} + +static void clearLayer(Layer *layer) { + if (layer != NULL) { + clearMatrix(&layer->weights); + clearMatrix(&layer->biases); + layer->activation = NULL; + } +} + +static void assignActivations(NeuralNetwork model) { + for (int i = 0; i < (int)model.numberOfLayers - 1; i++) { + model.layers[i].activation = relu; + } + + if (model.numberOfLayers > 0) + model.layers[model.numberOfLayers - 1].activation = softmax; +} + +NeuralNetwork loadModel(const char *path) { + NeuralNetwork model = {NULL, 0}; + FILE *file = fopen(path, "rb"); + + if (file != NULL) { + if (checkFileHeader(file)) { + unsigned int inputDimension = readDimension(file); + unsigned int outputDimension = readDimension(file); + + while (inputDimension > 0 && outputDimension > 0) { + Layer layer = readLayer(file, inputDimension, outputDimension); + Layer *layerBuffer = NULL; + + if (isEmptyLayer(layer)) { + clearLayer(&layer); + clearModel(&model); + break; } - } - return matrix; -} + layerBuffer = (Layer *)realloc( + model.layers, (model.numberOfLayers + 1) * sizeof(Layer)); -static Matrix forward(const NeuralNetwork model, Matrix inputBatch) -{ - Matrix result = inputBatch; - - if(result.buffer != NULL) - { - for(int i = 0; i < model.numberOfLayers; i++) - { - Matrix biasResult; - Matrix weightResult; - - weightResult = multiply(model.layers[i].weights, result); - clearMatrix(&result); - biasResult = add(model.layers[i].biases, weightResult); - clearMatrix(&weightResult); - - if(model.layers[i].activation != NULL) - model.layers[i].activation(&biasResult); - result = biasResult; + if (layerBuffer != NULL) + model.layers = layerBuffer; + else { + clearModel(&model); + break; } - } - return result; + model.layers[model.numberOfLayers] = layer; + model.numberOfLayers++; + + inputDimension = outputDimension; + outputDimension = readDimension(file); + } + } + fclose(file); + + assignActivations(model); + } + + return model; } -unsigned char *argmax(const Matrix matrix) -{ - unsigned char *maxIdx = NULL; +static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], + unsigned int count) { + Matrix matrix = {0, 0, NULL}; // falsch herum - if(matrix.rows > 0 && matrix.cols > 0) - { - maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols); + if (count > 0 && images != NULL) { + matrix = createMatrix(images[0].height * images[0].width, count); - if(maxIdx != NULL) - { - for(int colIdx = 0; colIdx < matrix.cols; colIdx++) - { - maxIdx[colIdx] = 0; - - for(int rowIdx = 1; rowIdx < matrix.rows; rowIdx++) - { - if(getMatrixAt(matrix, rowIdx, colIdx) > getMatrixAt(matrix, maxIdx[colIdx], colIdx)) - maxIdx[colIdx] = rowIdx; - } - } + if (matrix.buffer != NULL) { + for (int i = 0; i < count; i++) { + for (int j = 0; j < images[i].width * images[i].height; j++) { + setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i); } + } } + } - return maxIdx; + return matrix; } -unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[], unsigned int numberOfImages) -{ - Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages); - Matrix outputBatch = forward(model, inputBatch); +static Matrix forward(const NeuralNetwork model, Matrix inputBatch) { + Matrix result = inputBatch; - unsigned char *result = argmax(outputBatch); - - clearMatrix(&outputBatch); - - return result; + if (result.buffer != NULL) { + for (int i = 0; i < model.numberOfLayers; i++) { + Matrix biasResult; + Matrix weightResult; + + weightResult = multiply(model.layers[i].weights, result); + clearMatrix(&result); + biasResult = add(model.layers[i].biases, weightResult); + clearMatrix(&weightResult); + + if (model.layers[i].activation != NULL) + model.layers[i].activation(&biasResult); + result = biasResult; + } + } + + return result; } -void clearModel(NeuralNetwork *model) -{ - if(model != NULL) - { - for(int i = 0; i < model->numberOfLayers; i++) - { - clearLayer(&model->layers[i]); +unsigned char *argmax(const Matrix matrix) { + unsigned char *maxIdx = NULL; + + if (matrix.rows > 0 && matrix.cols > 0) { + maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols); + + if (maxIdx != NULL) { + for (int colIdx = 0; colIdx < matrix.cols; colIdx++) { + maxIdx[colIdx] = 0; + + for (int rowIdx = 1; rowIdx < matrix.rows; rowIdx++) { + if (getMatrixAt(matrix, rowIdx, colIdx) > + getMatrixAt(matrix, maxIdx[colIdx], colIdx)) + maxIdx[colIdx] = rowIdx; } - model->layers = NULL; - model->numberOfLayers = 0; + } } + } + + return maxIdx; +} + +unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[], + unsigned int numberOfImages) { + Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages); + Matrix outputBatch = forward(model, inputBatch); + + unsigned char *result = argmax(outputBatch); + + clearMatrix(&outputBatch); + + return result; +} + +void clearModel(NeuralNetwork *model) { + if (model != NULL) { + for (int i = 0; i < model->numberOfLayers; i++) { + clearLayer(&model->layers[i]); + } + model->layers = NULL; + model->numberOfLayers = 0; + } } \ No newline at end of file diff --git a/neuralNetworkTests.c b/neuralNetworkTests.c index 21ab370..9d2ac42 100644 --- a/neuralNetworkTests.c +++ b/neuralNetworkTests.c @@ -1,242 +1,308 @@ +#include "neuralNetwork.h" +#include "unity.h" +#include #include #include #include -#include -#include "unity.h" -#include "neuralNetwork.h" +static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) { + FILE *f = fopen(path, "wb"); + if (f == NULL) + return; -static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) -{ - // TODO -} + /* 1) Header: exakt das String, ohne '\n' oder abschließendes '\0' */ + const char header[] = "__info2_neural_network_file_format__"; + fwrite(header, sizeof(char), strlen(header), f); -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}; - 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}}; + /* Wenn es keine Layer gibt, kein Dimensionspaar schreiben (loadModel + wird beim Lesen dann 0 zurückgeben). Aber wir können auch frühzeitig + mit einem 0-Int terminieren — beides ist in Ordnung. */ + if (nn.numberOfLayers == 0) { + /* optional: schreibe ein 0 als next outputDimension (nicht nötig) */ + int zero = 0; + fwrite(&zero, sizeof(int), 1, f); + fclose(f); + return; + } - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=2}; - NeuralNetwork netUnderTest; + /* 2) Für die erste Layer schreiben wir inputDimension und outputDimension */ + /* inputDimension == weights.cols, outputDimension == weights.rows */ + int inputDim = (int)nn.layers[0].weights.cols; + int outputDim = (int)nn.layers[0].weights.rows; + fwrite(&inputDim, sizeof(int), 1, f); + fwrite(&outputDim, sizeof(int), 1, f); - prepareNeuralNetworkFile(path, expectedNet); + /* 3) Für jede Layer in Reihenfolge: Gewichte (output x input), Biases (output + x 1). Zwischen Layern wird nur die nächste outputDimension (int) + geschrieben. */ + for (int i = 0; i < nn.numberOfLayers; i++) { + Layer layer = nn.layers[i]; - netUnderTest = loadModel(path); - remove(path); + int wrows = (int)layer.weights.rows; + int wcols = (int)layer.weights.cols; + int wcount = wrows * wcols; + int bcount = + layer.biases.rows * layer.biases.cols; /* normalerweise rows * 1 */ - TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers, netUnderTest.numberOfLayers); - clearModel(&netUnderTest); -} - -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}; - MatrixType biasBuffer[] = {7, 8, 9}; - Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1}; - Layer layers[] = {{.weights=weights, .biases=biases}}; - - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; - NeuralNetwork netUnderTest; - - prepareNeuralNetworkFile(path, expectedNet); - - netUnderTest = loadModel(path); - remove(path); - - TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0); - TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows); - TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols); - clearModel(&netUnderTest); -} - -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}; - MatrixType biasBuffer[] = {7, 8, 9}; - Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1}; - Layer layers[] = {{.weights=weights, .biases=biases}}; - - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; - NeuralNetwork netUnderTest; - - prepareNeuralNetworkFile(path, expectedNet); - - netUnderTest = loadModel(path); - remove(path); - - TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0); - TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.rows, netUnderTest.layers[0].biases.rows); - TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.cols, netUnderTest.layers[0].biases.cols); - clearModel(&netUnderTest); -} - -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}; - MatrixType biasBuffer[] = {7, 8, 9}; - Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1}; - Layer layers[] = {{.weights=weights, .biases=biases}}; - - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; - NeuralNetwork netUnderTest; - - prepareNeuralNetworkFile(path, expectedNet); - - netUnderTest = loadModel(path); - remove(path); - - TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0); - TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows); - TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols); - int n = netUnderTest.layers[0].weights.rows * netUnderTest.layers[0].weights.cols; - TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].weights.buffer, netUnderTest.layers[0].weights.buffer, n); - clearModel(&netUnderTest); -} - -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}; - MatrixType biasBuffer[] = {7, 8, 9}; - Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1}; - Layer layers[] = {{.weights=weights, .biases=biases}}; - - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; - NeuralNetwork netUnderTest; - - prepareNeuralNetworkFile(path, expectedNet); - - netUnderTest = loadModel(path); - remove(path); - - TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0); - TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, netUnderTest.layers[0].weights.rows); - TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, netUnderTest.layers[0].weights.cols); - int n = netUnderTest.layers[0].biases.rows * netUnderTest.layers[0].biases.cols; - TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].biases.buffer, netUnderTest.layers[0].biases.buffer, n); - clearModel(&netUnderTest); -} - -void test_loadModelFailsOnWrongFileTag(void) -{ - const char *path = "some_nn_test_file.info2"; - NeuralNetwork netUnderTest; - FILE *file = fopen(path, "wb"); - - if(file != NULL) - { - const char *fileTag = "info2_neural_network_file_format"; - - fwrite(fileTag, sizeof(char), strlen(fileTag), file); - - fclose(file); + /* Gewichte (MatrixType binär) */ + if (wcount > 0 && layer.weights.buffer != NULL) { + fwrite(layer.weights.buffer, sizeof(MatrixType), (size_t)wcount, f); } - netUnderTest = loadModel(path); - - remove(path); - - TEST_ASSERT_NULL(netUnderTest.layers); - TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers); -} - -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}; - MatrixType biasBuffer[] = {7, 8, 9}; - Matrix biases = {.buffer=biasBuffer, .rows=3, .cols=1}; - Layer layers[] = {{.weights=weights, .biases=biases}}; - - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; - NeuralNetwork netUnderTest; - - prepareNeuralNetworkFile(path, expectedNet); - - netUnderTest = loadModel(path); - remove(path); - - TEST_ASSERT_NOT_NULL(netUnderTest.layers); - TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0); - clearModel(&netUnderTest); - TEST_ASSERT_NULL(netUnderTest.layers); - TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers); -} - -static void someActivation(Matrix *matrix) -{ - for(int i = 0; i < matrix->rows * matrix->cols; i++) - { - matrix->buffer[i] = fabs(matrix->buffer[i]); + /* Biases (MatrixType binär) */ + if (bcount > 0 && layer.biases.buffer != NULL) { + fwrite(layer.biases.buffer, sizeof(MatrixType), (size_t)bcount, f); } + + /* Für die nächste Layer: falls vorhanden, schreibe deren outputDimension */ + if (i + 1 < nn.numberOfLayers) { + int nextOutput = (int)nn.layers[i + 1].weights.rows; + fwrite(&nextOutput, sizeof(int), 1, f); + } else { + /* Letzte Layer: wir können das Ende signalisieren, indem wir ein 0 + schreiben. loadModel liest dann outputDimension = 0 und beendet die + Schleife. */ + int zero = 0; + fwrite(&zero, sizeof(int), 1, f); + } + } + + fclose(f); } -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}}; - 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}; - 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}; - unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2); - TEST_ASSERT_NOT_NULL(predictedLabels); - int n = (int)(sizeof(expectedLabels) / sizeof(expectedLabels[0])); - TEST_ASSERT_EQUAL_UINT8_ARRAY(expectedLabels, predictedLabels, n); - free(predictedLabels); +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}; + 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}}; + + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 2}; + NeuralNetwork netUnderTest; + + prepareNeuralNetworkFile(path, expectedNet); + + netUnderTest = loadModel(path); + remove(path); + + TEST_ASSERT_EQUAL_INT(expectedNet.numberOfLayers, + netUnderTest.numberOfLayers); + clearModel(&netUnderTest); +} + +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}; + MatrixType biasBuffer[] = {7, 8, 9}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; + + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; + NeuralNetwork netUnderTest; + + prepareNeuralNetworkFile(path, expectedNet); + + netUnderTest = loadModel(path); + remove(path); + + TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0); + TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, + netUnderTest.layers[0].weights.rows); + TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, + netUnderTest.layers[0].weights.cols); + clearModel(&netUnderTest); +} + +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}; + MatrixType biasBuffer[] = {7, 8, 9}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; + + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; + NeuralNetwork netUnderTest; + + prepareNeuralNetworkFile(path, expectedNet); + + netUnderTest = loadModel(path); + remove(path); + + TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0); + TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.rows, + netUnderTest.layers[0].biases.rows); + TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].biases.cols, + netUnderTest.layers[0].biases.cols); + clearModel(&netUnderTest); +} + +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}; + MatrixType biasBuffer[] = {7, 8, 9}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; + + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; + NeuralNetwork netUnderTest; + + prepareNeuralNetworkFile(path, expectedNet); + + netUnderTest = loadModel(path); + remove(path); + + TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0); + TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, + netUnderTest.layers[0].weights.rows); + TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, + netUnderTest.layers[0].weights.cols); + int n = + netUnderTest.layers[0].weights.rows * netUnderTest.layers[0].weights.cols; + TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].weights.buffer, + netUnderTest.layers[0].weights.buffer, n); + clearModel(&netUnderTest); +} + +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}; + MatrixType biasBuffer[] = {7, 8, 9}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; + + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; + NeuralNetwork netUnderTest; + + prepareNeuralNetworkFile(path, expectedNet); + + netUnderTest = loadModel(path); + remove(path); + + TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0); + TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.rows, + netUnderTest.layers[0].weights.rows); + TEST_ASSERT_EQUAL_INT(expectedNet.layers[0].weights.cols, + netUnderTest.layers[0].weights.cols); + int n = + netUnderTest.layers[0].biases.rows * netUnderTest.layers[0].biases.cols; + TEST_ASSERT_EQUAL_INT_ARRAY(expectedNet.layers[0].biases.buffer, + netUnderTest.layers[0].biases.buffer, n); + clearModel(&netUnderTest); +} + +void test_loadModelFailsOnWrongFileTag(void) { + const char *path = "some_nn_test_file.info2"; + NeuralNetwork netUnderTest; + FILE *file = fopen(path, "wb"); + + if (file != NULL) { + const char *fileTag = "info2_neural_network_file_format"; + + fwrite(fileTag, sizeof(char), strlen(fileTag), file); + + fclose(file); + } + + netUnderTest = loadModel(path); + + remove(path); + + TEST_ASSERT_NULL(netUnderTest.layers); + TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers); +} + +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}; + MatrixType biasBuffer[] = {7, 8, 9}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; + + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; + NeuralNetwork netUnderTest; + + prepareNeuralNetworkFile(path, expectedNet); + + netUnderTest = loadModel(path); + remove(path); + + TEST_ASSERT_NOT_NULL(netUnderTest.layers); + TEST_ASSERT_TRUE(netUnderTest.numberOfLayers > 0); + clearModel(&netUnderTest); + TEST_ASSERT_NULL(netUnderTest.layers); + TEST_ASSERT_EQUAL_INT(0, netUnderTest.numberOfLayers); +} + +static void someActivation(Matrix *matrix) { + for (int i = 0; i < matrix->rows * matrix->cols; i++) { + matrix->buffer[i] = fabs(matrix->buffer[i]); + } +} + +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}}; + 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}; + 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}; + unsigned char *predictedLabels = predict(netUnderTest, inputImages, 2); + TEST_ASSERT_NOT_NULL(predictedLabels); + int n = (int)(sizeof(expectedLabels) / sizeof(expectedLabels[0])); + TEST_ASSERT_EQUAL_UINT8_ARRAY(expectedLabels, predictedLabels, n); + free(predictedLabels); } void setUp(void) { - // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden + // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden } void tearDown(void) { - // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden + // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden } -int main() -{ - UNITY_BEGIN(); +int main() { + UNITY_BEGIN(); - printf("\n============================\nNeural network tests\n============================\n"); - RUN_TEST(test_loadModelReturnsCorrectNumberOfLayers); - RUN_TEST(test_loadModelReturnsCorrectWeightDimensions); - RUN_TEST(test_loadModelReturnsCorrectBiasDimensions); - RUN_TEST(test_loadModelReturnsCorrectWeights); - RUN_TEST(test_loadModelReturnsCorrectBiases); - RUN_TEST(test_loadModelFailsOnWrongFileTag); - RUN_TEST(test_clearModelSetsMembersToNull); - RUN_TEST(test_predictReturnsCorrectLabels); + printf("\n============================\nNeural network " + "tests\n============================\n"); + RUN_TEST(test_loadModelReturnsCorrectNumberOfLayers); + RUN_TEST(test_loadModelReturnsCorrectWeightDimensions); + RUN_TEST(test_loadModelReturnsCorrectBiasDimensions); + RUN_TEST(test_loadModelReturnsCorrectWeights); + RUN_TEST(test_loadModelReturnsCorrectBiases); + RUN_TEST(test_loadModelFailsOnWrongFileTag); + RUN_TEST(test_clearModelSetsMembersToNull); + RUN_TEST(test_predictReturnsCorrectLabels); - return UNITY_END(); + return UNITY_END(); } \ No newline at end of file