Author SHA1 Message Date
Anja Freudenreich 447e77a7bf updating 2026-09-17 14:46:11 +02:00
Anja Freudenreich a6749f07d1 updating 2026-09-17 14:45:27 +02:00
36 changed files with 62 additions and 41 deletions
+60 -3
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@@ -1,4 +1,61 @@
mnist # ---> C
runTests # Prerequisites
*.d
# Object files
*.o *.o
*.exe *.ko
*.obj
*.elf
# Linker output
*.ilk
*.map
*.exp
# Precompiled Headers
*.gch
*.pch
# Libraries
*.lib
*.la
*.lo
# Shared objects (inc. Windows DLLs)
*.dll
*.so
*.so.*
*.dylib
# Executables
*.exe
*.out
*.app
*.i*86
*.x86_64
*.hex
Startcode/mnist
Startcode/runTests
# Debug files
*.dSYM/
*.su
*.idb
*.pdb
# Kernel Module Compile Results
*.mod*
*.cmd
.tmp_versions/
modules.order
Module.symvers
Mkfile.old
dkms.conf
# IDE folders
.vscode/
.idea/
# macOS
.DS_Store
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@@ -170,7 +170,7 @@ NeuralNetwork loadModel(const char *path)
static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count) static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
{ {
Matrix matrix = {0, 0, NULL}; Matrix matrix = {NULL, 0, 0};
if(count > 0 && images != NULL) if(count > 0 && images != NULL)
{ {
@@ -5,46 +5,10 @@
#include "unity.h" #include "unity.h"
#include "neuralNetwork.h" #include "neuralNetwork.h"
static void writeLayer(FILE *file, const Matrix weights, const Matrix biases, unsigned int inputDim)
{
unsigned int outputDim = (unsigned int)weights.rows;
fwrite(&outputDim, sizeof(unsigned int), 1, file);
if (weights.buffer != NULL)
fwrite(weights.buffer, sizeof(MatrixType), outputDim * inputDim, file);
if (biases.buffer != NULL)
fwrite(biases.buffer, sizeof(MatrixType), outputDim, file);
}
static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn) static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
{ {
FILE *file = fopen(path, "wb"); // TODO
if (!file) return;
const char tag[] = "__info2_neural_network_file_format__";
fwrite(tag, sizeof(char), strlen(tag), file);
if (nn.numberOfLayers == 0)
{
unsigned int zero = 0;
fwrite(&zero, sizeof(unsigned int), 1, file);
fclose(file);
return;
}
unsigned int inputDim = (unsigned int)nn.layers[0].weights.cols;
fwrite(&inputDim, sizeof(unsigned int), 1, file);
for (int i = 0; i < nn.numberOfLayers; i++)
{
writeLayer(file, nn.layers[i].weights, nn.layers[i].biases, inputDim);
inputDim = (unsigned int)nn.layers[i].weights.rows;
}
unsigned int zero = 0;
fwrite(&zero, sizeof(unsigned int), 1, file);
fclose(file);
} }
void test_loadModelReturnsCorrectNumberOfLayers(void) void test_loadModelReturnsCorrectNumberOfLayers(void)