forked from freudenreichan/info2Praktikum-NeuronalesNetz
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5 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 2436240736 | |||
| fde82f2d9a | |||
| 0fc70f982c | |||
| b271c865cb | |||
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077c6def78 |
Binary file not shown.
77
imageInput.c
77
imageInput.c
@ -5,95 +5,18 @@
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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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#define FILE_HEADER_SIZE (sizeof(FILE_HEADER_STRING)-1)
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// TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
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// TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
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FILE *checkFile(const char *path)
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{
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FILE *datei = fopen(path,"rb");
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if (datei == NULL)
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{
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perror("Datei konnte nicht geoeffnet werden");
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return NULL;
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}
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char buffer[FILE_HEADER_SIZE+1];
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if (fread(buffer,1,FILE_HEADER_SIZE,datei)!=FILE_HEADER_SIZE)
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{
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perror("Header konnte nicht eingelessen werden");
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fclose(datei);
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return NULL;
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}
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buffer[FILE_HEADER_SIZE] = '\0';
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if (strcmp(buffer,FILE_HEADER_STRING)!=0)
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{
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printf("Falscher Dateikopf");
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//printf("\n%s",buffer);
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//printf("\n%s",FILE_HEADER_STRING);
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//printf("\n%d",strcmp(buffer,FILE_HEADER_STRING));
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fclose(datei);
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return NULL;
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}
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return datei;
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}
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// TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
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// TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
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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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FILE *datei = checkFile(path);
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if (datei==NULL)
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{
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return NULL;
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}
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unsigned short image_count, width, height;
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fread(&image_count,sizeof(unsigned short),1,datei);
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fread(&width,sizeof(unsigned short),1,datei);
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fread(&height,sizeof(unsigned short),1,datei);
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//printf("%u Bilder und %u mal %u",image_count,width,height);
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GrayScaleImageSeries *series = NULL;
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GrayScaleImageSeries *series = NULL;
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series = malloc(sizeof(GrayScaleImageSeries));
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series->count = image_count;
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series->images = malloc(image_count*sizeof(GrayScaleImage));
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series->labels = malloc(image_count*sizeof(unsigned char));
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for(unsigned short i = 0;i<image_count;i++)
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{
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series->images[i].width = width;
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series->images[i].height = height;
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series->images[i].buffer = malloc(width*height);
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}
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for(unsigned short i = 0;i<image_count;i++)
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{
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for (unsigned int j=0;j<(width*height);j++)
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{
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fread(&series->images[i].buffer[j],1,1,datei);
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}
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fread(&series->labels[i],1,1,datei);
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//printf("%d\n",series->labels[i]);
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}
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fclose(datei);
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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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// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
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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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if(series == NULL)
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{
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printf("Serie nicht vorhanden\n");
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return;
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}
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unsigned short anzahl = series->count;
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for(unsigned short i = 0;i<anzahl;i++)
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{
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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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printf("Serie freigegeben\n");
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return;
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}
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}
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@ -19,5 +19,5 @@ typedef struct
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GrayScaleImageSeries *readImages(const char *path);
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GrayScaleImageSeries *readImages(const char *path);
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void clearSeries(GrayScaleImageSeries *series);
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void clearSeries(GrayScaleImageSeries *series);
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FILE *checkFile(const char *path);
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#endif
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#endif
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@ -70,7 +70,7 @@ void test_readImagesReturnsCorrectImageHeight(void)
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GrayScaleImageSeries *series = NULL;
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GrayScaleImageSeries *series = NULL;
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const unsigned short expectedHeight = 10;
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const unsigned short expectedHeight = 10;
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const char *path = "testFile.info2";
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const char *path = "testFile.info2";
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prepareImageFile(path,8, expectedHeight, 2, 1);
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prepareImageFile(path, 8, expectedHeight, 2, 1);
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series = readImages(path);
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series = readImages(path);
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TEST_ASSERT_NOT_NULL(series);
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TEST_ASSERT_NOT_NULL(series);
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TEST_ASSERT_NOT_NULL(series->images);
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TEST_ASSERT_NOT_NULL(series->images);
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4
main.c
4
main.c
@ -1,4 +1,3 @@
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#include <stdio.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <stdlib.h>
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#include "imageInput.h"
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#include "imageInput.h"
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@ -7,8 +6,6 @@
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int main(int argc, char *argv[])
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int main(int argc, char *argv[])
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{
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{
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//readImages("mnist_test.info2");
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const unsigned int windowWidth = 800;
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const unsigned int windowWidth = 800;
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const unsigned int windowHeight = 600;
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const unsigned int windowHeight = 600;
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@ -68,5 +65,4 @@ int main(int argc, char *argv[])
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}
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}
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return exitCode;
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return exitCode;
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}
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}
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103
matrix.c
103
matrix.c
@ -1,128 +1,35 @@
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#include <stdlib.h>
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#include <stdlib.h>
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#include <string.h>
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#include <string.h>
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#include "matrix.h"
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#include "matrix.h"
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#include <stdio.h>
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// TODO Matrix-Funktionen implementieren
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// Matrix erzeugen
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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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{
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{
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Matrix matrix;
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matrix.buffer = NULL;
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matrix.rows = 0;
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matrix.cols = 0;
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if (rows == 0 || cols == 0)
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return matrix; // leere Matrix
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matrix.buffer = (MatrixType *)malloc(rows * cols * sizeof(MatrixType));
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if (!matrix.buffer)
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return matrix; // Speicher konnte nicht reserviert werden
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matrix.rows = rows;
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matrix.cols = cols;
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// Initialisiere alle Werte auf UNDEFINED_MATRIX_VALUE
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for (unsigned int i = 0; i < rows * cols; i++)
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matrix.buffer[i] = UNDEFINED_MATRIX_VALUE;
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return matrix;
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}
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}
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// Matrix Speicher freigeben
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void clearMatrix(Matrix *matrix)
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void clearMatrix(Matrix *matrix)
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{
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{
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if (!matrix) return;
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if (matrix->buffer)
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free(matrix->buffer);
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matrix->buffer = NULL;
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matrix->rows = 0;
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matrix->cols = 0;
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}
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}
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// Wert setzen
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void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
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void setMatrixAt(MatrixType value, Matrix matrix, unsigned int rowIdx, unsigned int colIdx)
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{
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{
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if (!matrix.buffer) return;
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if (rowIdx >= matrix.rows || colIdx >= matrix.cols) return;
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matrix.buffer[rowIdx * matrix.cols + colIdx] = value;
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}
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}
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// Wert auslesen
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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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{
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{
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if (!matrix.buffer) return UNDEFINED_MATRIX_VALUE;
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if (rowIdx >= matrix.rows || colIdx >= matrix.cols) return UNDEFINED_MATRIX_VALUE;
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return matrix.buffer[rowIdx * matrix.cols + colIdx];
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}
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}
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// Matrizen addieren
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Matrix add(const Matrix m1, const Matrix m2)
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Matrix add(const Matrix matrix1, const Matrix matrix2)
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{
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{
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if (!m1.buffer || !m2.buffer) return createMatrix(0,0);
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// gleiche Dimension
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if (m1.rows == m2.rows && m1.cols == m2.cols)
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{
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Matrix result = createMatrix(m1.rows, m1.cols);
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if (!result.buffer) return result;
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for (unsigned int r = 0; r < m1.rows; r++)
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for (unsigned int c = 0; c < m1.cols; c++)
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result.buffer[r * result.cols + c] = m1.buffer[r * m1.cols + c] + m2.buffer[r * m2.cols + c];
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return result;
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}
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// Matrix2 ist ein Spaltenvektor
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if (m1.rows == m2.rows && m2.cols == 1)
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{
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Matrix result = createMatrix(m1.rows, m1.cols);
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if (!result.buffer) return result;
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for (unsigned int r = 0; r < m1.rows; r++)
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for (unsigned int c = 0; c < m1.cols; c++)
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result.buffer[r * result.cols + c] = m1.buffer[r * m1.cols + c] + m2.buffer[r];
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return result;
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}
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// Matrix1 ist ein Spaltenvektor
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if (m1.rows == m2.rows && m1.cols == 1)
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{
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Matrix result = createMatrix(m2.rows, m2.cols);
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if (!result.buffer) return result;
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for (unsigned int r = 0; r < m2.rows; r++)
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for (unsigned int c = 0; c < m2.cols; c++)
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result.buffer[r * result.cols + c] = m1.buffer[r] + m2.buffer[r * m2.cols + c];
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return result;
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}
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// passt nicht
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return createMatrix(0,0);
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}
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}
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// Matrizen multiplizieren
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Matrix multiply(const Matrix m1, const Matrix m2)
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Matrix multiply(const Matrix matrix1, const Matrix matrix2)
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{
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{
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if (!m1.buffer || !m2.buffer) return createMatrix(0,0);
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if (m1.cols != m2.rows) return createMatrix(0,0);
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Matrix result = createMatrix(m1.rows, m2.cols);
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if (!result.buffer) return result;
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for (unsigned int r = 0; r < m1.rows; r++)
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for (unsigned int c = 0; c < m2.cols; c++)
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{
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MatrixType sum = 0;
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for (unsigned int k = 0; k < m1.cols; k++)
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sum += m1.buffer[r * m1.cols + k] * m2.buffer[k * m2.cols + c];
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result.buffer[r * result.cols + c] = sum;
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}
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return result;
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}
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}
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7
matrix.h
7
matrix.h
@ -6,13 +6,6 @@
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typedef float MatrixType;
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typedef float MatrixType;
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// TODO Matrixtyp definieren
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// TODO Matrixtyp definieren
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typedef struct
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{
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MatrixType *buffer;
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unsigned int rows;
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unsigned int cols;
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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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@ -71,6 +71,32 @@ void test_addFailsOnDifferentInputDimensions(void)
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TEST_ASSERT_EQUAL_UINT32(0, result.cols);
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TEST_ASSERT_EQUAL_UINT32(0, result.cols);
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}
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}
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void test_addSupportsBroadcasting(void)
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|
{
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MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
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MatrixType buffer2[] = {7, 8};
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Matrix matrix1 = {.rows=2, .cols=3, .buffer=buffer1};
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Matrix matrix2 = {.rows=2, .cols=1, .buffer=buffer2};
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Matrix result1 = add(matrix1, matrix2);
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Matrix result2 = add(matrix2, matrix1);
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float expectedResults[] = {8, 9, 10, 12, 13, 14};
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TEST_ASSERT_EQUAL_UINT32(matrix1.rows, result1.rows);
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TEST_ASSERT_EQUAL_UINT32(matrix1.cols, result1.cols);
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TEST_ASSERT_EQUAL_UINT32(matrix1.rows, result2.rows);
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TEST_ASSERT_EQUAL_UINT32(matrix1.cols, result2.cols);
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TEST_ASSERT_EQUAL_INT(sizeof(expectedResults)/sizeof(expectedResults[0]), result1.rows * result1.cols);
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TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, result1.buffer, result1.cols * result1.rows);
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TEST_ASSERT_EQUAL_INT(sizeof(expectedResults)/sizeof(expectedResults[0]), result2.rows * result2.cols);
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TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, result2.buffer, result2.cols * result2.rows);
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|
free(result1.buffer);
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|
free(result2.buffer);
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||||||
|
}
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||||||
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void test_multiplyReturnsCorrectResults(void)
|
void test_multiplyReturnsCorrectResults(void)
|
||||||
{
|
{
|
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MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
|
MatrixType buffer1[] = {1, 2, 3, 4, 5, 6};
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@ -138,7 +164,7 @@ void test_setMatrixAtFailsOnIndicesOutOfRange(void)
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|||||||
Matrix matrixToTest = {.rows=2, .cols=3, .buffer=buffer};
|
Matrix matrixToTest = {.rows=2, .cols=3, .buffer=buffer};
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|
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setMatrixAt(-1, matrixToTest, 2, 3);
|
setMatrixAt(-1, matrixToTest, 2, 3);
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TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, matrixToTest.buffer, matrixToTest.cols * matrixToTest.rows);
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TEST_ASSERT_EQUAL_FLOAT_ARRAY(expectedResults, matrixToTest.buffer, sizeof(buffer)/sizeof(MatrixType));
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}
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}
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||||||
|
|
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void setUp(void) {
|
void setUp(void) {
|
||||||
@ -159,6 +185,7 @@ int main()
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|||||||
RUN_TEST(test_clearMatrixSetsMembersToNull);
|
RUN_TEST(test_clearMatrixSetsMembersToNull);
|
||||||
RUN_TEST(test_addReturnsCorrectResult);
|
RUN_TEST(test_addReturnsCorrectResult);
|
||||||
RUN_TEST(test_addFailsOnDifferentInputDimensions);
|
RUN_TEST(test_addFailsOnDifferentInputDimensions);
|
||||||
|
RUN_TEST(test_addSupportsBroadcasting);
|
||||||
RUN_TEST(test_multiplyReturnsCorrectResults);
|
RUN_TEST(test_multiplyReturnsCorrectResults);
|
||||||
RUN_TEST(test_multiplyFailsOnWrongInputDimensions);
|
RUN_TEST(test_multiplyFailsOnWrongInputDimensions);
|
||||||
RUN_TEST(test_getMatrixAtReturnsCorrectResult);
|
RUN_TEST(test_getMatrixAtReturnsCorrectResult);
|
||||||
|
|||||||
@ -197,7 +197,7 @@ static Matrix forward(const NeuralNetwork model, Matrix inputBatch)
|
|||||||
|
|
||||||
if(result.buffer != NULL)
|
if(result.buffer != NULL)
|
||||||
{
|
{
|
||||||
for(int i = 0; i < model.numberOfLayers; i++)
|
for(int i = 0; i < model.numberOfLayers; i++)
|
||||||
{
|
{
|
||||||
Matrix biasResult;
|
Matrix biasResult;
|
||||||
Matrix weightResult;
|
Matrix weightResult;
|
||||||
|
|||||||
@ -8,58 +8,7 @@
|
|||||||
|
|
||||||
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
|
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
|
||||||
{
|
{
|
||||||
// TODO : Fehlerbehandlung
|
// TODO
|
||||||
// Öffne die Datei zum Schreiben im Binärmodus
|
|
||||||
FILE *file = fopen(path, "wb");
|
|
||||||
if (!file) return;
|
|
||||||
|
|
||||||
// Schreibe den Datei-Tag
|
|
||||||
const char *tag = "__info2_neural_network_file_format__";
|
|
||||||
fwrite(tag, 1, strlen(tag), file);
|
|
||||||
|
|
||||||
// Schreibe die Anzahl der Layer
|
|
||||||
if (nn.numberOfLayers == 0) {
|
|
||||||
fclose(file);
|
|
||||||
return;
|
|
||||||
}
|
|
||||||
|
|
||||||
// Schreibe die Eingabe- und Ausgabegrößen des Netzwerks
|
|
||||||
int input = nn.layers[0].weights.cols;
|
|
||||||
int output = nn.layers[0].weights.rows;
|
|
||||||
|
|
||||||
fwrite(&input, sizeof(int), 1, file);
|
|
||||||
fwrite(&output, sizeof(int), 1, file);
|
|
||||||
|
|
||||||
// Schreibe die Layer-Daten
|
|
||||||
for (int i = 0; i < nn.numberOfLayers; i++)
|
|
||||||
{
|
|
||||||
const Layer *layer = &nn.layers[i];
|
|
||||||
int out = layer->weights.rows;
|
|
||||||
int in = layer->weights.cols;
|
|
||||||
|
|
||||||
|
|
||||||
fwrite(layer->weights.buffer, sizeof(MatrixType), out * in, file);
|
|
||||||
|
|
||||||
|
|
||||||
fwrite(layer->biases.buffer, sizeof(MatrixType), out * 1, file);
|
|
||||||
|
|
||||||
if (i + 1 < nn.numberOfLayers)
|
|
||||||
{
|
|
||||||
int nextOut = nn.layers[i + 1].weights.rows;
|
|
||||||
fwrite(&nextOut, sizeof(int), 1, file);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
fclose(file);
|
|
||||||
|
|
||||||
// Debuging-Ausgabe
|
|
||||||
printf("prepareNeuralNetworkFile: Datei '%s' erstellt mit %u Layer(n)\n", path, nn.numberOfLayers);
|
|
||||||
for (unsigned int i = 0; i < nn.numberOfLayers; i++) {
|
|
||||||
Layer layer = nn.layers[i];
|
|
||||||
printf("Layer %u: weights (%u x %u), biases (%u x %u)\n",
|
|
||||||
i, layer.weights.rows, layer.weights.cols, layer.biases.rows, layer.biases.cols);
|
|
||||||
}
|
|
||||||
|
|
||||||
}
|
}
|
||||||
|
|
||||||
void test_loadModelReturnsCorrectNumberOfLayers(void)
|
void test_loadModelReturnsCorrectNumberOfLayers(void)
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user