forked from freudenreichan/info2Praktikum-NeuronalesNetz
fix prep function
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833a4c3c79
commit
70a7e6d20d
1 file changed
+5
-5
@@ -13,17 +13,17 @@ static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
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if (fd == NULL)
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if (fd == NULL)
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return;
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return;
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fwrite(FILE_HEADER_STRING, sizeof(FILE_HEADER_STRING) - 1, 1, fd);
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fwrite(FILE_HEADER_STRING, strlen(FILE_HEADER_STRING), 1, fd);
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// fwrite(&nn, sizeof(nn), 1, fd);
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// fwrite(&nn, sizeof(nn), 1, fd);
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fwrite(&(nn.layers[0].weights.cols), sizeof(nn.layers[0].weights.cols), 1, fd);
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fwrite(&(nn.layers[0].weights.cols), sizeof(nn.layers[0].weights.cols), 1, fd);
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for (int i = 0; i < nn.numberOfLayers; ++i)
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for (int i = 0; i < nn.numberOfLayers; ++i)
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{
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{
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fwrite(&(nn.layers[i].weights.rows), sizeof(nn.layers[i].weights.rows), 1, fd);
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fwrite(&(nn.layers[i].weights.rows), sizeof(nn.layers[i].weights.rows), 1, fd);
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fwrite(&(nn.layers[i].weights.buffer), sizeof(MatrixType) * nn.layers[i].weights.rows * nn.layers[i].weights.cols, 1, fd);
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fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), nn.layers[i].weights.rows * nn.layers[i].weights.cols, fd);
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fwrite(&(nn.layers[i].biases.buffer), sizeof(MatrixType) * nn.layers[i].biases.rows, 1, fd);
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fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), nn.layers[i].biases.rows, fd);
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}
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}
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const float zeroooooo = 0.0F;
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const int zeroooooo = 0;
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fwrite(&zeroooooo, sizeof(MatrixType), 1, fd);
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fwrite(&zeroooooo, sizeof(int), 1, fd);
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fclose(fd);
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fclose(fd);
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}
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}
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