knoppal101327/NeuNeuronalesNetz
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AD005\z004z3ez 2025-11-24 15:21:25 +01:00
parent 8e5c32f197
commit ed983fc250
1 changed files with 5 additions and 5 deletions
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10
neuralNetworkTests.c
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@ -9,7 +9,7 @@
static void writeWeights(Layer layer, FILE *file)
{
unsigned int n = (unsigned int)layer.weights.rows * layer.weights.cols; //col und row müssen nicht extra eingelesen werden, da loadModel die Dimensionen aus der Fkt loadModel() selbst liest
unsigned int n = (unsigned int)layer.weights.rows * layer.weights.cols; //col und row müssen nicht extra eingelesen werden, da loadModel die Dimensionen selbst aus der Datei liest
fwrite(layer.weights.buffer, sizeof(MatrixType), n, file);
}
@ -21,7 +21,7 @@ static void writeBiases(Layer layer, FILE *file)
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
{
//file erstellen und zum binärschreiben öffnen
//file erstellen und zum Binärschreiben öffnen
FILE *file = fopen(path, "wb");
if(file == NULL)
return;
@ -41,7 +41,7 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
unsigned int inputDim = (unsigned int)nn.layers[0].weights.cols;
fwrite(&inputDim, sizeof(unsigned int), 1, file);
//für jede Schicht: Dimension, Gewichte und Biases einlesen
//für jede Schicht: Dimension, Gewichte und Biases schreiben
for (unsigned int i = 0; i < nn.numberOfLayers; i++)
{
Layer layer = nn.layers[i];
@ -49,11 +49,11 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
unsigned int outputDim = (unsigned int)layer.weights.rows;
fwrite(&outputDim, sizeof(unsigned int), 1, file);
//dimensionen festlegen(weights)
//Weight-Matrixwerte schreiben
writeWeights(layer, file);
//dimension festlegen(bias)
//Bias-Vektorwerte schreiben
writeBiases(layer, file);