generated from freudenreichan/info2Praktikum-NeuronalesNetz
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0f28ee3f02 | ||
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bb96c8f78a | ||
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cfb3848fe2 |
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@@ -131,14 +131,4 @@ Matrix multiply(const Matrix matrix1, const Matrix matrix2)
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}
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}
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}
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}
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return result;
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return result;
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}
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void writeMatrix(Matrix matrix, FILE *file)//Added for neuralNetworkTests
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{
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for(int i = 0; i < matrix.rows; i++)
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{
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for(int j = 0; j < matrix.cols; j++)
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{
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fprintf(file, (char*)(matrix.buffer + (j+i)*sizeof(MatrixType)));
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}
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}
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}
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}
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@@ -21,7 +21,6 @@ void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned
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MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx);
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MatrixType getMatrixAt(const Matrix matrix, unsigned int rowIdx, unsigned int colIdx);
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Matrix add(const Matrix matrix1, const Matrix matrix2);
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Matrix add(const Matrix matrix1, const Matrix matrix2);
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Matrix multiply(const Matrix matrix1, const Matrix matrix2);
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Matrix multiply(const Matrix matrix1, const Matrix matrix2);
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void writeMatrix(Matrix matrix, FILE *file);//Added for neuralNetworkTests
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#endif
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#endif
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+1
-1
@@ -170,7 +170,7 @@ NeuralNetwork loadModel(const char *path)
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static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
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static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
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{
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{
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Matrix matrix = {NULL, 0, 0};
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Matrix matrix = {0, 0, NULL}; // TODO changed this line to fit our functionality
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if(count > 0 && images != NULL)
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if(count > 0 && images != NULL)
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{
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{
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+22
-9
@@ -4,20 +4,33 @@
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#include <math.h>
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#include <math.h>
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#include "unity.h"
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#include "unity.h"
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#include "neuralNetwork.h"
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#include "neuralNetwork.h"
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#include "matrix.h"
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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 *testDatei = fopen(path, "w");
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// TODO
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fprintf(testDatei, "__info2_neural_network_file_format__");
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FILE* file = fopen(path, "wb");
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for(int i = 0; i < nn.numberOfLayers; i++)
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if(file == NULL) {
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{
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printf("Failed to open file");
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fprintf(testDatei, "\n");
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return;
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writeMatrix(nn.layers[i].weights, testDatei);
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writeMatrix(nn.layers[i].biases, testDatei);
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}
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}
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fclose(testDatei);
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const char* header = "__info2_neural_network_file_format__";
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fwrite(header, sizeof(const char), strlen(header), file);
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fwrite(&(nn.numberOfLayers), sizeof(unsigned int), 1, file);
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fwrite(&(nn.numberOfLayers), sizeof(unsigned int), 1, file);
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for(int i = 0; i < nn.numberOfLayers; i++) {
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//write everything to do with weights
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fwrite(&(nn.layers[i].weights.rows), sizeof(unsigned int), 1, file);
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fwrite(&(nn.layers[i].weights.cols), sizeof(unsigned int), 1, file);
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fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType), nn.layers[i].weights.rows * nn.layers[i].weights.cols, file);
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//write everything to do with biases
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fwrite(&(nn.layers[i].biases.rows), sizeof(unsigned int), 1, file);
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fwrite(&(nn.layers[i].biases.cols), sizeof(unsigned int), 1, file);
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fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType), nn.layers[i].biases.rows * nn.layers[i].biases.cols, file);
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}
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fclose(file);
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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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