forked from freudenreichan/info2Praktikum-NeuronalesNetz
prepare datei fertig
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@ -7,7 +7,45 @@
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static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
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static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
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{
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{
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FILE *f = fopen(path, "wb");
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if (!f) return;
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const char *tag = "__info2_neural_network_file_format__";
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fwrite(tag, 1, strlen(tag), f);
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if (nn.numberOfLayers == 0) {
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fclose(f);
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return;
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} // Alle Tests prüfen
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int input = nn.layers[0].weights.cols;
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int output = nn.layers[0].weights.rows;
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fwrite(&input, sizeof(int), 1, f);
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fwrite(&output, sizeof(int), 1, f);
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for (int i = 0; i < nn.numberOfLayers; i++)
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{
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const Layer *layer = &nn.layers[i];
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int out = layer->weights.rows;
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int in = layer->weights.cols;
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fwrite(layer->weights.buffer, sizeof(MatrixType), out * in, f);
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fwrite(layer->biases.buffer, sizeof(MatrixType), out * 1, f);
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if (i + 1 < nn.numberOfLayers)
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{
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int nextOut = nn.layers[i + 1].weights.rows;
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fwrite(&nextOut, sizeof(int), 1, f);
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}
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}
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fclose(f);
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}
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}
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void test_loadModelReturnsCorrectNumberOfLayers(void)
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void test_loadModelReturnsCorrectNumberOfLayers(void)
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