soppbe99185/info2Praktikum-NeuronalesNetz
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Benedikt Sopp 2025-11-25 10:48:36 +01:00
parent 52dc266ad7
commit c6f9776cd7
4 changed files with 131 additions and 61 deletions
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57
imageInputTests.c
View File

@ -1,41 +1,43 @@
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include "unity.h"
#include "imageInput.h"
static void prepareImageFile(const char *path, unsigned short int width, unsigned short int height, unsigned int short numberOfImages, unsigned char label)
{
FILE *file = fopen(path, "wb");
if(file != NULL)
if (file != NULL)
{
const char *fileTag = "__info2_image_file_format__";
GrayScalePixelType *zeroBuffer = (GrayScalePixelType *)calloc(numberOfImages * width * height, sizeof(GrayScalePixelType));
GrayScalePixelType *buffer = (GrayScalePixelType *)calloc(numberOfImages * width * height, sizeof(GrayScalePixelType));
if(zeroBuffer != NULL)
if (buffer != NULL)
{
fwrite(fileTag, sizeof(fileTag[0]), strlen(fileTag), file);
for (int i = 0; i < width * height; i++)
{
buffer[i] = (GrayScalePixelType)i; // füllen des buffers mit Graustufen des Pixel für Test
}
fwrite(fileTag, 1, strlen(fileTag), file);
fwrite(&numberOfImages, sizeof(numberOfImages), 1, file);
fwrite(&width, sizeof(width), 1, file);
fwrite(&height, sizeof(height), 1, file);
for(int i = 0; i < numberOfImages; i++)
for (int i = 0; i < numberOfImages; i++)
{
fwrite(zeroBuffer, sizeof(GrayScalePixelType), width * height, file);
fwrite(buffer, sizeof(GrayScalePixelType), width * height, file);
fwrite(&label, sizeof(unsigned char), 1, file);
}
free(zeroBuffer);
free(buffer);
}
fclose(file);
}
}
void test_readImagesReturnsCorrectNumberOfImages(void)
{
GrayScaleImageSeries *series = NULL;
@ -92,7 +94,8 @@ void test_readImagesReturnsCorrectLabels(void)
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_NOT_NULL(series->labels);
TEST_ASSERT_EQUAL_UINT16(2, series->count);
for (int i = 0; i < 2; i++) {
for (int i = 0; i < 2; i++)
{
TEST_ASSERT_EQUAL_UINT8(expectedLabel, series->labels[i]);
}
clearSeries(series);
@ -110,7 +113,7 @@ void test_readImagesFailsOnWrongFileTag(void)
{
const char *path = "testFile.info2";
FILE *file = fopen(path, "w");
if(file != NULL)
if (file != NULL)
{
fprintf(file, "some_tag ");
fclose(file);
@ -119,11 +122,36 @@ void test_readImagesFailsOnWrongFileTag(void)
remove(path);
}
void setUp(void) {
// Test der Hilfsfunktionen
void test_read_GrayScale_Pixel(void)
{
GrayScaleImageSeries *series = NULL;
const char *path = "testFile.info2";
prepareImageFile(path, 8, 8, 1, 1);
series = readImages(path);
TEST_ASSERT_NOT_NULL(series);
TEST_ASSERT_NOT_NULL(series->images);
TEST_ASSERT_EQUAL_UINT16(1, series->count);
for (int i = 0; i < (8 * 8); i++)
{
TEST_ASSERT_EQUAL_UINT8((GrayScalePixelType)i, series->images->buffer[i]);
}
clearSeries(series);
remove(path);
}
void setUp(void)
{
// Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
}
void tearDown(void) {
void tearDown(void)
{
// Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
}
@ -138,6 +166,7 @@ int main()
RUN_TEST(test_readImagesReturnsCorrectLabels);
RUN_TEST(test_readImagesReturnsNullOnNotExistingPath);
RUN_TEST(test_readImagesFailsOnWrongFileTag);
RUN_TEST(test_read_GrayScale_Pixel);
return UNITY_END();
}

4
matrix.h
View File

@ -11,8 +11,8 @@ typedef struct Matrix
{
unsigned int rows;
unsigned int cols;
MatrixType *data;
#define buffer data
MatrixType *buffer;
} Matrix;

2
neuralNetwork.c
View File

@ -170,7 +170,7 @@ NeuralNetwork loadModel(const char *path)
static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
{
Matrix matrix = {NULL, 0, 0};
Matrix matrix = {0, 0, NULL};
if(count > 0 && images != NULL)
{

127
neuralNetworkTests.c
View File

@ -5,10 +5,49 @@
#include "unity.h"
#include "neuralNetwork.h"
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
{
// TODO
FILE *f = fopen(path, "wb");
if (!f) return;
const char *tag = "__info2_neural_network_file_format__";
fwrite(tag, 1, strlen(tag), f);
if (nn.numberOfLayers == 0) {
fclose(f);
return;
} // In localmodel Struktur Testdateu aufruf:
// Header --> Input Dim --> Output Dim
// i. Layer weights --> biases --> nächste Dim
int input = nn.layers[0].weights.cols;
int output = nn.layers[0].weights.rows;
fwrite(&input, sizeof(int), 1, f);
fwrite(&output, sizeof(int), 1, f);
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, f);
fwrite(layer->biases.buffer, sizeof(MatrixType), out * 1, f);
if (i + 1 < nn.numberOfLayers)
{
int nextOut = nn.layers[i + 1].weights.rows;
fwrite(&nextOut, sizeof(int), 1, f);
}
}
fclose(f);
}
void test_loadModelReturnsCorrectNumberOfLayers(void)
@ -16,15 +55,15 @@ 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 +79,12 @@ 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 +102,12 @@ 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 +125,12 @@ 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 +150,12 @@ 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 +177,7 @@ 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 +198,12 @@ 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 +220,7 @@ 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 +231,23 @@ 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 +255,13 @@ 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
}