linzto104707/info2Praktikum-NeuronalesNetz
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Implement prepareNeuralNetworkFile

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Tobias Linz committed 2026-11-04 19:34:28 +01:00
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commit 1aaec0e5f2
3 files changed
+112 -57
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Startcode/neuralNetworkTests.c
+45 -44
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@@ -5,10 +5,9 @@
#include "unity.h"
#include "neuralNetwork.h"
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
{
// TODO
saveModel(path, nn);
}
void test_loadModelReturnsCorrectNumberOfLayers(void)
@@ -16,15 +15,15 @@ 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 +39,12 @@ 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 +62,12 @@ 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 +85,12 @@ 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 +110,12 @@ 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);
@@ -159,12 +158,12 @@ 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);
@@ -192,23 +191,23 @@ 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]));
@@ -216,12 +215,14 @@ void test_predictReturnsCorrectLabels(void)
free(predictedLabels);
}
void setUp(void) {
// Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
void setUp(void)
{
// TODO: Falls notwendig, kann hier Vorbereitungsarbeit gemacht werden
}
void tearDown(void) {
// Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
void tearDown(void)
{
// TODO: Hier kann Bereinigungsarbeit nach jedem Test durchgeführt werden
}
int main()
@@ -239,4 +240,4 @@ int main()
RUN_TEST(test_predictReturnsCorrectLabels);
return UNITY_END();
}
}