From 632bdeb9b986a465c5409843d6cffd52ab8d1d7c Mon Sep 17 00:00:00 2001 From: maxgrf Date: Mon, 24 Nov 2025 12:09:28 +0100 Subject: [PATCH] angepasst --- neuralNetworkTests.c | 152 +++++++++++++++++++++++++++++++------------ 1 file changed, 109 insertions(+), 43 deletions(-) diff --git a/neuralNetworkTests.c b/neuralNetworkTests.c index 21ab370..ebc4b34 100644 --- a/neuralNetworkTests.c +++ b/neuralNetworkTests.c @@ -5,10 +5,74 @@ #include "unity.h" #include "neuralNetwork.h" - static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) { - // TODO + /* + typedef struct + { + Matrix weights; + Matrix biases; + ActivationFunctionType activation; + } Layer; + + typedef struct + { + Layer *layers; + unsigned int numberOfLayers; + } NeuralNetwork; + + */ + FILE *file = fopen(path, "wb"); + if (!file) + return; + + //--------------------------------------------------------------------------- + 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) @@ -16,15 +80,15 @@ 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}; + 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}}; + 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 expectedNet = {.layers = layers, .numberOfLayers = 2}; NeuralNetwork netUnderTest; prepareNeuralNetworkFile(path, expectedNet); @@ -40,12 +104,12 @@ 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}; + 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}}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; NeuralNetwork netUnderTest; prepareNeuralNetworkFile(path, expectedNet); @@ -63,12 +127,12 @@ 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}; + 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}}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; NeuralNetwork netUnderTest; prepareNeuralNetworkFile(path, expectedNet); @@ -86,12 +150,12 @@ 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}; + 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}}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; NeuralNetwork netUnderTest; prepareNeuralNetworkFile(path, expectedNet); @@ -111,12 +175,12 @@ 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}; + 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}}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; NeuralNetwork netUnderTest; prepareNeuralNetworkFile(path, expectedNet); @@ -138,7 +202,7 @@ void test_loadModelFailsOnWrongFileTag(void) NeuralNetwork netUnderTest; FILE *file = fopen(path, "wb"); - if(file != NULL) + if (file != NULL) { const char *fileTag = "info2_neural_network_file_format"; @@ -159,12 +223,12 @@ 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}; + 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}}; + Matrix biases = {.buffer = biasBuffer, .rows = 3, .cols = 1}; + Layer layers[] = {{.weights = weights, .biases = biases}}; - NeuralNetwork expectedNet = {.layers=layers, .numberOfLayers=1}; + NeuralNetwork expectedNet = {.layers = layers, .numberOfLayers = 1}; NeuralNetwork netUnderTest; prepareNeuralNetworkFile(path, expectedNet); @@ -181,7 +245,7 @@ void test_clearModelSetsMembersToNull(void) static void someActivation(Matrix *matrix) { - for(int i = 0; i < matrix->rows * matrix->cols; i++) + for (int i = 0; i < matrix->rows * matrix->cols; i++) { matrix->buffer[i] = fabs(matrix->buffer[i]); } @@ -192,23 +256,23 @@ 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}}; + 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}; + 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}; + 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])); @@ -216,11 +280,13 @@ void test_predictReturnsCorrectLabels(void) free(predictedLabels); } -void setUp(void) { +void setUp(void) +{ // Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden } -void tearDown(void) { +void tearDown(void) +{ // Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden }