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prepareNeuralNetworkFile angepasst, sodass Test PASS läuft

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Laila Mueller 2025-11-16 22:00:43 +01:00
parent 28a968e2c9
commit 53a7828cf0
1 changed files with 13 additions and 7 deletions
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16
info2praktikum-neuronalesnetz/neuralNetworkTests.c
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@ -22,12 +22,19 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
for (unsigned int i = 0; i < nn.numberOfLayers; i++) { for (unsigned int i = 0; i < nn.numberOfLayers; i++) {
Layer layer = nn.layers[i]; Layer layer = nn.layers[i];
unsigned int inputDim = layer.weights.cols; // Anzahl Eingänge
unsigned int outputDim = layer.weights.rows; // Anzahl Ausgänge
// Dimensionen schreiben if(i == 0){
// beim ersten Layer inputDim und outputDim schreiben
unsigned int inputDim = layer.weights.cols; // Anzahl Eingänge
fwrite(&inputDim, sizeof(unsigned int), 1, file); fwrite(&inputDim, sizeof(unsigned int), 1, file);
unsigned int outputDim = layer.weights.rows; // Anzahl Ausgänge
fwrite(&outputDim, sizeof(unsigned int), 1, file); fwrite(&outputDim, sizeof(unsigned int), 1, file);
}
else{
// bei allen anderen Layern nur outputDim schreiben
unsigned int outputDim = layer.weights.rows; // Anzahl Ausgänge
fwrite(&outputDim, sizeof(unsigned int), 1, file);
}
// Gewichte schreiben // Gewichte schreiben
size_t weightCount = layer.weights.rows * layer.weights.cols; size_t weightCount = layer.weights.rows * layer.weights.cols;
@ -38,8 +45,7 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
fwrite(layer.biases.buffer, sizeof(MatrixType), biasCount, file); fwrite(layer.biases.buffer, sizeof(MatrixType), biasCount, file);
} }
// End-Marker (outputDim = 0)
// End-Marker (inputDim = 0)
unsigned int zero = 0; unsigned int zero = 0;
fwrite(&zero, sizeof(unsigned int), 1, file); fwrite(&zero, sizeof(unsigned int), 1, file);