generated from freudenreichan/info2Praktikum-NeuronalesNetz
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@ -164,7 +164,7 @@ NeuralNetwork loadModel(const char *path)
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assignActivations(model);
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}
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printf("%d\n", model.numberOfLayers);
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return model;
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}
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@ -1 +0,0 @@
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make clean && make && make neuralNetworkTests
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@ -8,41 +8,9 @@
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static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
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{
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FILE *file = fopen(path, "wb");
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if(file != NULL){
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const char *fileTag = "__info2_neural_network_file_format__";
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// Write file header
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fwrite(fileTag, sizeof(char), strlen(fileTag), file);
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// Write the input dimension of the first layer
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if(nn.numberOfLayers > 0){
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fwrite(&nn.layers[0].weights.cols, sizeof(int), 1, file);
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}
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// Write dimensions and data for each layer
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for(int i = 0; i < nn.numberOfLayers; i++){
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// Write output dimension (rows of weights)
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fwrite(&nn.layers[i].weights.rows, sizeof(int), 1, file);
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// Write weight matrix data
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int weightSize = nn.layers[i].weights.rows * nn.layers[i].weights.cols;
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fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), weightSize, file);
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// Write bias matrix data
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int biasSize = nn.layers[i].biases.rows * nn.layers[i].biases.cols;
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fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), biasSize, file);
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}
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// Write terminating 0 to signal end of layers
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int zero = 0;
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fwrite(&zero, sizeof(int), 1, file);
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fclose(file);
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}
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// TODO
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}
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void test_loadModelReturnsCorrectNumberOfLayers(void)
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{
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const char *path = "some__nn_test_file.info2";
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