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Kristin 2025-11-25 10:48:24 +01:00
parent efa260ccbe
commit 9606b5a03e
1 changed files with 201 additions and 234 deletions
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neuralNetwork.c
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@ -1,268 +1,235 @@
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <string.h>
#include "neuralNetwork.h" #include "neuralNetwork.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#define BUFFER_SIZE 100 #define BUFFER_SIZE 100
#define FILE_HEADER_STRING "__info2_neural_network_file_format__" #define FILE_HEADER_STRING "__info2_neural_network_file_format__"
static void softmax(Matrix *matrix) static void softmax(Matrix *matrix) {
{ if (matrix->cols > 0) {
if(matrix->cols > 0) double *colSums = (double *)calloc(matrix->cols, sizeof(double));
{
double *colSums = (double *)calloc(matrix->cols, sizeof(double));
if(colSums != NULL) if (colSums != NULL) {
{ for (int colIdx = 0; colIdx < matrix->cols; colIdx++) {
for(int colIdx = 0; colIdx < matrix->cols; colIdx++) for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) {
{ MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx));
for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) setMatrixAt(expValue, *matrix, rowIdx, colIdx);
{ colSums[colIdx] += expValue;
MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx));
setMatrixAt(expValue, *matrix, rowIdx, colIdx);
colSums[colIdx] += expValue;
}
}
for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
{
for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
{
MatrixType normalizedValue = getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx];
setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx);
}
}
free(colSums);
} }
} }
}
static void relu(Matrix *matrix) for (int colIdx = 0; colIdx < matrix->cols; colIdx++) {
{ for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) {
for(int i = 0; i < matrix->rows * matrix->cols; i++) MatrixType normalizedValue =
{ getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx];
matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0; setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx);
}
}
static int checkFileHeader(FILE *file)
{
int isValid = 0;
int fileHeaderLen = strlen(FILE_HEADER_STRING);
char buffer[BUFFER_SIZE] = {0};
if(BUFFER_SIZE-1 < fileHeaderLen)
fileHeaderLen = BUFFER_SIZE-1;
if(fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen)
isValid = strcmp(buffer, FILE_HEADER_STRING) == 0;
return isValid;
}
static unsigned int readDimension(FILE *file)
{
int dimension = 0;
if(fread(&dimension, sizeof(int), 1, file) != 1)
dimension = 0;
return dimension;
}
static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols)
{
Matrix matrix = createMatrix(rows, cols);
if(matrix.buffer != NULL)
{
if(fread(matrix.buffer, sizeof(MatrixType), rows*cols, file) != rows*cols)
clearMatrix(&matrix);
}
return matrix;
}
static Layer readLayer(FILE *file, unsigned int inputDimension, unsigned int outputDimension)
{
Layer layer;
layer.weights = readMatrix(file, outputDimension, inputDimension);
layer.biases = readMatrix(file, outputDimension, 1);
return layer;
}
static int isEmptyLayer(const Layer layer)
{
return layer.biases.cols == 0 || layer.biases.rows == 0 || layer.biases.buffer == NULL || layer.weights.rows == 0 || layer.weights.cols == 0 || layer.weights.buffer == NULL;
}
static void clearLayer(Layer *layer)
{
if(layer != NULL)
{
clearMatrix(&layer->weights);
clearMatrix(&layer->biases);
layer->activation = NULL;
}
}
static void assignActivations(NeuralNetwork model)
{
for(int i = 0; i < (int)model.numberOfLayers-1; i++)
{
model.layers[i].activation = relu;
}
if(model.numberOfLayers > 0)
model.layers[model.numberOfLayers-1].activation = softmax;
}
NeuralNetwork loadModel(const char *path)
{
NeuralNetwork model = {NULL, 0};
FILE *file = fopen(path, "rb");
if(file != NULL)
{
if(checkFileHeader(file))
{
unsigned int inputDimension = readDimension(file);
unsigned int outputDimension = readDimension(file);
while(inputDimension > 0 && outputDimension > 0)
{
Layer layer = readLayer(file, inputDimension, outputDimension);
Layer *layerBuffer = NULL;
if(isEmptyLayer(layer))
{
clearLayer(&layer);
clearModel(&model);
break;
}
layerBuffer = (Layer *)realloc(model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
if(layerBuffer != NULL)
model.layers = layerBuffer;
else
{
clearModel(&model);
break;
}
model.layers[model.numberOfLayers] = layer;
model.numberOfLayers++;
inputDimension = outputDimension;
outputDimension = readDimension(file);
}
} }
fclose(file); }
free(colSums);
assignActivations(model);
} }
}
return model;
} }
static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count) static void relu(Matrix *matrix) {
{ for (int i = 0; i < matrix->rows * matrix->cols; i++) {
Matrix matrix = {NULL, 0, 0}; matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0;
}
}
if(count > 0 && images != NULL) static int checkFileHeader(FILE *file) {
{ int isValid = 0;
matrix = createMatrix(images[0].height * images[0].width, count); int fileHeaderLen = strlen(FILE_HEADER_STRING);
char buffer[BUFFER_SIZE] = {0};
if(matrix.buffer != NULL) if (BUFFER_SIZE - 1 < fileHeaderLen)
{ fileHeaderLen = BUFFER_SIZE - 1;
for(int i = 0; i < count; i++)
{ if (fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen)
for(int j = 0; j < images[i].width * images[i].height; j++) isValid = strcmp(buffer, FILE_HEADER_STRING) == 0;
{
setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i); return isValid;
} }
}
static unsigned int readDimension(FILE *file) {
int dimension = 0;
if (fread(&dimension, sizeof(int), 1, file) != 1)
dimension = 0;
return dimension;
}
static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols) {
Matrix matrix = createMatrix(rows, cols);
if (matrix.buffer != NULL) {
if (fread(matrix.buffer, sizeof(MatrixType), rows * cols, file) !=
rows * cols)
clearMatrix(&matrix);
}
return matrix;
}
static Layer readLayer(FILE *file, unsigned int inputDimension,
unsigned int outputDimension) {
Layer layer;
layer.weights = readMatrix(file, outputDimension, inputDimension);
layer.biases = readMatrix(file, outputDimension, 1);
return layer;
}
static int isEmptyLayer(const Layer layer) {
return layer.biases.cols == 0 || layer.biases.rows == 0 ||
layer.biases.buffer == NULL || layer.weights.rows == 0 ||
layer.weights.cols == 0 || layer.weights.buffer == NULL;
}
static void clearLayer(Layer *layer) {
if (layer != NULL) {
clearMatrix(&layer->weights);
clearMatrix(&layer->biases);
layer->activation = NULL;
}
}
static void assignActivations(NeuralNetwork model) {
for (int i = 0; i < (int)model.numberOfLayers - 1; i++) {
model.layers[i].activation = relu;
}
if (model.numberOfLayers > 0)
model.layers[model.numberOfLayers - 1].activation = softmax;
}
NeuralNetwork loadModel(const char *path) {
NeuralNetwork model = {NULL, 0};
FILE *file = fopen(path, "rb");
if (file != NULL) {
if (checkFileHeader(file)) {
unsigned int inputDimension = readDimension(file);
unsigned int outputDimension = readDimension(file);
while (inputDimension > 0 && outputDimension > 0) {
Layer layer = readLayer(file, inputDimension, outputDimension);
Layer *layerBuffer = NULL;
if (isEmptyLayer(layer)) {
clearLayer(&layer);
clearModel(&model);
break;
} }
}
return matrix; layerBuffer = (Layer *)realloc(
} model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
static Matrix forward(const NeuralNetwork model, Matrix inputBatch) if (layerBuffer != NULL)
{ model.layers = layerBuffer;
Matrix result = inputBatch; else {
clearModel(&model);
if(result.buffer != NULL) break;
{
for(int i = 0; i < model.numberOfLayers; i++)
{
Matrix biasResult;
Matrix weightResult;
weightResult = multiply(model.layers[i].weights, result);
clearMatrix(&result);
biasResult = add(model.layers[i].biases, weightResult);
clearMatrix(&weightResult);
if(model.layers[i].activation != NULL)
model.layers[i].activation(&biasResult);
result = biasResult;
} }
}
return result; model.layers[model.numberOfLayers] = layer;
model.numberOfLayers++;
inputDimension = outputDimension;
outputDimension = readDimension(file);
}
}
fclose(file);
assignActivations(model);
}
return model;
} }
unsigned char *argmax(const Matrix matrix) static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[],
{ unsigned int count) {
unsigned char *maxIdx = NULL; Matrix matrix = {0, 0, NULL};
if(matrix.rows > 0 && matrix.cols > 0) if (count > 0 && images != NULL) {
{ matrix = createMatrix(images[0].height * images[0].width, count);
maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols);
if(maxIdx != NULL) if (matrix.buffer != NULL) {
{ for (int i = 0; i < count; i++) {
for(int colIdx = 0; colIdx < matrix.cols; colIdx++) for (int j = 0; j < images[i].width * images[i].height; j++) {
{ setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i);
maxIdx[colIdx] = 0;
for(int rowIdx = 1; rowIdx < matrix.rows; rowIdx++)
{
if(getMatrixAt(matrix, rowIdx, colIdx) > getMatrixAt(matrix, maxIdx[colIdx], colIdx))
maxIdx[colIdx] = rowIdx;
}
}
} }
}
} }
}
return maxIdx; return matrix;
} }
unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[], unsigned int numberOfImages) static Matrix forward(const NeuralNetwork model, Matrix inputBatch) {
{ Matrix result = inputBatch;
Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages);
Matrix outputBatch = forward(model, inputBatch);
unsigned char *result = argmax(outputBatch); if (result.buffer != NULL) {
for (int i = 0; i < model.numberOfLayers; i++) {
clearMatrix(&outputBatch); Matrix biasResult;
Matrix weightResult;
return result;
weightResult = multiply(model.layers[i].weights, result);
clearMatrix(&result);
biasResult = add(model.layers[i].biases, weightResult);
clearMatrix(&weightResult);
if (model.layers[i].activation != NULL)
model.layers[i].activation(&biasResult);
result = biasResult;
}
}
return result;
} }
void clearModel(NeuralNetwork *model) unsigned char *argmax(const Matrix matrix) {
{ unsigned char *maxIdx = NULL;
if(model != NULL)
{ if (matrix.rows > 0 && matrix.cols > 0) {
for(int i = 0; i < model->numberOfLayers; i++) maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols);
{
clearLayer(&model->layers[i]); if (maxIdx != NULL) {
for (int colIdx = 0; colIdx < matrix.cols; colIdx++) {
maxIdx[colIdx] = 0;
for (int rowIdx = 1; rowIdx < matrix.rows; rowIdx++) {
if (getMatrixAt(matrix, rowIdx, colIdx) >
getMatrixAt(matrix, maxIdx[colIdx], colIdx))
maxIdx[colIdx] = rowIdx;
} }
model->layers = NULL; }
model->numberOfLayers = 0;
} }
}
return maxIdx;
}
unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
unsigned int numberOfImages) {
Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages);
Matrix outputBatch = forward(model, inputBatch);
unsigned char *result = argmax(outputBatch);
clearMatrix(&outputBatch);
return result;
}
void clearModel(NeuralNetwork *model) {
if (model != NULL) {
for (int i = 0; i < model->numberOfLayers; i++) {
clearLayer(&model->layers[i]);
}
model->layers = NULL;
model->numberOfLayers = 0;
}
} }