forked from freudenreichan/info2Praktikum-NeuronalesNetz
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3a9d8275a8
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bbb0ea1cf5
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12825cc1d3
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633ee723f4
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7ea80137b0 | ||
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6ba9ba3195 | ||
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f4427d2892 | ||
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84b65525a6 |
+94
-4
@@ -6,17 +6,107 @@
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#define BUFFER_SIZE 100
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#define BUFFER_SIZE 100
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#define FILE_HEADER_STRING "__info2_image_file_format__"
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#define FILE_HEADER_STRING "__info2_image_file_format__"
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// TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
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//Datei öffnen, Header, Anzahl, Höhe und Breite lesen, geöffnete Datei zurückgeben
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static FILE* openAndReadShort (const char *path, unsigned short *count, unsigned short *width, unsigned short *height) {
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FILE *file = fopen(path, "rb");
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if (!file) {
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return NULL;
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}
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size_t headerLength = strlen(FILE_HEADER_STRING);
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char *header = malloc (headerLength + 1);
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if(!header) {
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return NULL;
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}
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// TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
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if (fread(header, sizeof(char), headerLength, file) != headerLength) {
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free (header);
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return NULL;
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}
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header[headerLength] = '\0';
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if (strcmp (header, FILE_HEADER_STRING) != 0) {
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free(header);
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return NULL;
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}
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free (header);
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fread(count, sizeof(unsigned short), 1, file);
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fread(width, sizeof(unsigned short), 1, file);
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fread(height, sizeof(unsigned short), 1, file);
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return file;
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}
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//Speicher anlegen und Pixel eines Bildes einlesen
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static GrayScaleImage* readPixles (FILE *file, unsigned short *width, unsigned short *height) {
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GrayScaleImage *image = malloc (sizeof(GrayScaleImage));
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image->width = *width;
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image->height = *height;
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image->buffer = malloc ((*width) * (*height) * sizeof(GrayScalePixelType));
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if (!image->buffer) {
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free(image);
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return NULL;
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}
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for (unsigned int i = 0; i < (*width) * (*height); i++) {
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unsigned char pixel;
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if (fread(&pixel, sizeof(unsigned char), 1, file) != 1) {
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free(image->buffer);
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free(image);
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return NULL;
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}
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image->buffer[i] = pixel;
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}
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return image;
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}
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//Ausführen von openAndReadShort, Anlegen des Speichers für Bilderserie, readPixles wird für jedes Bild ausgeführt
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//Nach jedem Bild wird das zugehörige Label gelesen, bei sämtlichen Fehlern wird NULL zurückgegeben und Speicher durch clearSeries bereinigt
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GrayScaleImageSeries *readImages(const char *path)
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GrayScaleImageSeries *readImages(const char *path)
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{
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{
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GrayScaleImageSeries *series = NULL;
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unsigned short count = 0, width = 0, height = 0;
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FILE *file = openAndReadShort(path, &count, &width, &height);
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if (file == 0) {
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return NULL;
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}
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GrayScaleImageSeries *series = malloc(sizeof(GrayScaleImageSeries));
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if (!series) {
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fclose(file);
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return NULL;
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}
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series->count = count;
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series->images = malloc(count * sizeof(GrayScaleImage));
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series->labels = malloc(count* sizeof(unsigned char));
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for (unsigned int i = 0; i < series->count; i++) {
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GrayScaleImage *image = readPixles(file, &width, &height);
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series->images[i] = *image;
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free(image);
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if (fread(&series->labels[i], sizeof(unsigned char), 1, file) != 1) {
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clearSeries(series);
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fclose(file);
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return NULL;
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}
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}
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fclose(file);
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return series;
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return series;
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}
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}
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// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
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//Bereinigt den Speicher
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void clearSeries(GrayScaleImageSeries *series)
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void clearSeries(GrayScaleImageSeries *series)
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{
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{
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for (unsigned int i = 0; i < series->count; i++) {
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free(series->images[i].buffer);
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}
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free(series->images);
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free(series->labels);
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free(series);
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}
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}
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@@ -67,14 +67,18 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
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return createMatrix(0, 0);
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return createMatrix(0, 0);
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}
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}
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// matrices not compatible
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if (matrix1.rows != matrix2.rows)
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{
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clearMatrix(&resMat);
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return resMat;
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}
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// check if broadcasting is possible
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if (matrix1.cols != matrix2.cols)
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if (matrix1.cols != matrix2.cols)
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{
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{
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if (matrix1.rows != matrix2.rows)
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// matrix1 is a vector
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{
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if (matrix1.cols == 1)
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clearMatrix(&resMat);
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return resMat;
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}
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else if (matrix1.cols == 1)
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{
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{
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// broadcast vector
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// broadcast vector
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for (size_t m = 0; m < matrix2.rows; m++)
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for (size_t m = 0; m < matrix2.rows; m++)
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@@ -86,6 +90,7 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
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}
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}
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return resMat;
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return resMat;
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}
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}
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// matrix2 is a vector
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else if (matrix2.cols == 1)
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else if (matrix2.cols == 1)
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{
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{
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// broadcast vector
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// broadcast vector
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@@ -98,6 +103,7 @@ Matrix add(const Matrix matrix1, const Matrix matrix2)
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}
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}
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return resMat;
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return resMat;
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}
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}
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// addition not possible
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else
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else
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{
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{
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clearMatrix(&resMat);
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clearMatrix(&resMat);
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@@ -8,10 +8,9 @@ typedef float MatrixType;
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// Matrixtyp
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// Matrixtyp
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typedef struct Matrix
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typedef struct Matrix
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{
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{
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MatrixType *buffer;
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size_t rows;
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size_t rows;
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size_t cols;
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size_t cols;
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MatrixType *buffer;
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} Matrix;
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} Matrix;
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Matrix createMatrix(unsigned int rows, unsigned int cols);
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Matrix createMatrix(unsigned int rows, unsigned int cols);
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+37
-1
@@ -5,10 +5,46 @@
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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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static void writeLayer(FILE *file, const Matrix weights, const Matrix biases, unsigned int inputDim)
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{
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unsigned int outputDim = (unsigned int)weights.rows;
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fwrite(&outputDim, sizeof(unsigned int), 1, file);
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if (weights.buffer != NULL)
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fwrite(weights.buffer, sizeof(MatrixType), outputDim * inputDim, file);
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if (biases.buffer != NULL)
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fwrite(biases.buffer, sizeof(MatrixType), outputDim, file);
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}
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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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// TODO
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FILE *file = fopen(path, "wb");
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if (!file) return;
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const char tag[] = "__info2_neural_network_file_format__";
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fwrite(tag, sizeof(char), strlen(tag), file);
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if (nn.numberOfLayers == 0)
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{
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unsigned int zero = 0;
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fwrite(&zero, sizeof(unsigned int), 1, file);
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fclose(file);
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return;
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}
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unsigned int inputDim = (unsigned int)nn.layers[0].weights.cols;
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fwrite(&inputDim, sizeof(unsigned int), 1, file);
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for (int i = 0; i < nn.numberOfLayers; i++)
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{
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writeLayer(file, nn.layers[i].weights, nn.layers[i].biases, inputDim);
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inputDim = (unsigned int)nn.layers[i].weights.rows;
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}
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unsigned int zero = 0;
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fwrite(&zero, sizeof(unsigned int), 1, file);
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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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@@ -0,0 +1 @@
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some_tag
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