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Kristin
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435
neuralNetwork.c
435
neuralNetwork.c
@ -1,268 +1,235 @@
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#include <stdlib.h>
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#include <stdio.h>
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#include <math.h>
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#include <string.h>
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#include "neuralNetwork.h"
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#define BUFFER_SIZE 100
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#define FILE_HEADER_STRING "__info2_neural_network_file_format__"
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static void softmax(Matrix *matrix)
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{
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if(matrix->cols > 0)
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{
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double *colSums = (double *)calloc(matrix->cols, sizeof(double));
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static void softmax(Matrix *matrix) {
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if (matrix->cols > 0) {
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double *colSums = (double *)calloc(matrix->cols, sizeof(double));
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if(colSums != NULL)
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{
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for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
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{
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for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
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{
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MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx));
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setMatrixAt(expValue, *matrix, rowIdx, colIdx);
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colSums[colIdx] += expValue;
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}
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}
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for(int colIdx = 0; colIdx < matrix->cols; colIdx++)
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{
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for(int rowIdx = 0; rowIdx < matrix->rows; rowIdx++)
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{
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MatrixType normalizedValue = getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx];
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setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx);
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}
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}
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free(colSums);
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if (colSums != NULL) {
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for (int colIdx = 0; colIdx < matrix->cols; colIdx++) {
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for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) {
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MatrixType expValue = exp(getMatrixAt(*matrix, rowIdx, colIdx));
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setMatrixAt(expValue, *matrix, rowIdx, colIdx);
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colSums[colIdx] += expValue;
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}
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}
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}
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}
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static void relu(Matrix *matrix)
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{
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for(int i = 0; i < matrix->rows * matrix->cols; i++)
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{
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matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0;
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}
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}
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static int checkFileHeader(FILE *file)
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{
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int isValid = 0;
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int fileHeaderLen = strlen(FILE_HEADER_STRING);
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char buffer[BUFFER_SIZE] = {0};
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if(BUFFER_SIZE-1 < fileHeaderLen)
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fileHeaderLen = BUFFER_SIZE-1;
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if(fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen)
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isValid = strcmp(buffer, FILE_HEADER_STRING) == 0;
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return isValid;
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}
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static unsigned int readDimension(FILE *file)
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{
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int dimension = 0;
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if(fread(&dimension, sizeof(int), 1, file) != 1)
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dimension = 0;
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return dimension;
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}
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static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols)
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{
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Matrix matrix = createMatrix(rows, cols);
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if(matrix.buffer != NULL)
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{
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if(fread(matrix.buffer, sizeof(MatrixType), rows*cols, file) != rows*cols)
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clearMatrix(&matrix);
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}
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return matrix;
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}
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static Layer readLayer(FILE *file, unsigned int inputDimension, unsigned int outputDimension)
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{
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Layer layer;
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layer.weights = readMatrix(file, outputDimension, inputDimension);
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layer.biases = readMatrix(file, outputDimension, 1);
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return layer;
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}
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static int isEmptyLayer(const Layer layer)
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{
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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;
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}
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static void clearLayer(Layer *layer)
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{
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if(layer != NULL)
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{
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clearMatrix(&layer->weights);
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clearMatrix(&layer->biases);
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layer->activation = NULL;
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}
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}
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static void assignActivations(NeuralNetwork model)
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{
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for(int i = 0; i < (int)model.numberOfLayers-1; i++)
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{
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model.layers[i].activation = relu;
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}
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if(model.numberOfLayers > 0)
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model.layers[model.numberOfLayers-1].activation = softmax;
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}
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NeuralNetwork loadModel(const char *path)
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{
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NeuralNetwork model = {NULL, 0};
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FILE *file = fopen(path, "rb");
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if(file != NULL)
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{
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if(checkFileHeader(file))
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{
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unsigned int inputDimension = readDimension(file);
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unsigned int outputDimension = readDimension(file);
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while(inputDimension > 0 && outputDimension > 0)
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{
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Layer layer = readLayer(file, inputDimension, outputDimension);
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Layer *layerBuffer = NULL;
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if(isEmptyLayer(layer))
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{
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clearLayer(&layer);
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clearModel(&model);
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break;
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}
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layerBuffer = (Layer *)realloc(model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
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if(layerBuffer != NULL)
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model.layers = layerBuffer;
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else
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{
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clearModel(&model);
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break;
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}
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model.layers[model.numberOfLayers] = layer;
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model.numberOfLayers++;
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inputDimension = outputDimension;
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outputDimension = readDimension(file);
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}
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for (int colIdx = 0; colIdx < matrix->cols; colIdx++) {
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for (int rowIdx = 0; rowIdx < matrix->rows; rowIdx++) {
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MatrixType normalizedValue =
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getMatrixAt(*matrix, rowIdx, colIdx) / colSums[colIdx];
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setMatrixAt(normalizedValue, *matrix, rowIdx, colIdx);
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}
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fclose(file);
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assignActivations(model);
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}
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free(colSums);
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}
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return model;
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}
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}
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static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[], unsigned int count)
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{
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Matrix matrix = {NULL, 0, 0};
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static void relu(Matrix *matrix) {
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for (int i = 0; i < matrix->rows * matrix->cols; i++) {
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matrix->buffer[i] = matrix->buffer[i] >= 0 ? matrix->buffer[i] : 0;
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}
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}
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if(count > 0 && images != NULL)
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{
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matrix = createMatrix(images[0].height * images[0].width, count);
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static int checkFileHeader(FILE *file) {
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int isValid = 0;
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int fileHeaderLen = strlen(FILE_HEADER_STRING);
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char buffer[BUFFER_SIZE] = {0};
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if(matrix.buffer != NULL)
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{
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for(int i = 0; i < count; i++)
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{
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for(int j = 0; j < images[i].width * images[i].height; j++)
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{
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setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i);
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}
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}
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if (BUFFER_SIZE - 1 < fileHeaderLen)
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fileHeaderLen = BUFFER_SIZE - 1;
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if (fread(buffer, sizeof(char), fileHeaderLen, file) == fileHeaderLen)
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isValid = strcmp(buffer, FILE_HEADER_STRING) == 0;
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return isValid;
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}
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static unsigned int readDimension(FILE *file) {
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int dimension = 0;
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if (fread(&dimension, sizeof(int), 1, file) != 1)
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dimension = 0;
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return dimension;
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}
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static Matrix readMatrix(FILE *file, unsigned int rows, unsigned int cols) {
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Matrix matrix = createMatrix(rows, cols);
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if (matrix.buffer != NULL) {
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if (fread(matrix.buffer, sizeof(MatrixType), rows * cols, file) !=
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rows * cols)
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clearMatrix(&matrix);
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}
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return matrix;
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}
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static Layer readLayer(FILE *file, unsigned int inputDimension,
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unsigned int outputDimension) {
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Layer layer;
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layer.weights = readMatrix(file, outputDimension, inputDimension);
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layer.biases = readMatrix(file, outputDimension, 1);
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return layer;
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}
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static int isEmptyLayer(const Layer layer) {
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return layer.biases.cols == 0 || layer.biases.rows == 0 ||
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layer.biases.buffer == NULL || layer.weights.rows == 0 ||
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layer.weights.cols == 0 || layer.weights.buffer == NULL;
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}
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static void clearLayer(Layer *layer) {
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if (layer != NULL) {
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clearMatrix(&layer->weights);
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clearMatrix(&layer->biases);
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layer->activation = NULL;
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}
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}
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static void assignActivations(NeuralNetwork model) {
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for (int i = 0; i < (int)model.numberOfLayers - 1; i++) {
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model.layers[i].activation = relu;
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}
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if (model.numberOfLayers > 0)
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model.layers[model.numberOfLayers - 1].activation = softmax;
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}
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NeuralNetwork loadModel(const char *path) {
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NeuralNetwork model = {NULL, 0};
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FILE *file = fopen(path, "rb");
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if (file != NULL) {
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if (checkFileHeader(file)) {
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unsigned int inputDimension = readDimension(file);
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unsigned int outputDimension = readDimension(file);
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while (inputDimension > 0 && outputDimension > 0) {
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Layer layer = readLayer(file, inputDimension, outputDimension);
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Layer *layerBuffer = NULL;
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if (isEmptyLayer(layer)) {
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clearLayer(&layer);
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clearModel(&model);
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break;
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}
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}
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return matrix;
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}
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layerBuffer = (Layer *)realloc(
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model.layers, (model.numberOfLayers + 1) * sizeof(Layer));
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static Matrix forward(const NeuralNetwork model, Matrix inputBatch)
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{
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Matrix result = inputBatch;
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if(result.buffer != NULL)
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{
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for(int i = 0; i < model.numberOfLayers; i++)
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{
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Matrix biasResult;
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Matrix weightResult;
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weightResult = multiply(model.layers[i].weights, result);
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clearMatrix(&result);
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biasResult = add(model.layers[i].biases, weightResult);
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clearMatrix(&weightResult);
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if(model.layers[i].activation != NULL)
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model.layers[i].activation(&biasResult);
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result = biasResult;
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if (layerBuffer != NULL)
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model.layers = layerBuffer;
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else {
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clearModel(&model);
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break;
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}
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}
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return result;
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model.layers[model.numberOfLayers] = layer;
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model.numberOfLayers++;
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inputDimension = outputDimension;
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outputDimension = readDimension(file);
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}
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}
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fclose(file);
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assignActivations(model);
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}
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return model;
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}
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unsigned char *argmax(const Matrix matrix)
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{
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unsigned char *maxIdx = NULL;
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static Matrix imageBatchToMatrixOfImageVectors(const GrayScaleImage images[],
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unsigned int count) {
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Matrix matrix = {0, 0, NULL};
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if(matrix.rows > 0 && matrix.cols > 0)
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{
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maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols);
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if (count > 0 && images != NULL) {
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matrix = createMatrix(images[0].height * images[0].width, count);
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if(maxIdx != NULL)
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{
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for(int colIdx = 0; colIdx < matrix.cols; colIdx++)
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{
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maxIdx[colIdx] = 0;
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for(int rowIdx = 1; rowIdx < matrix.rows; rowIdx++)
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{
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if(getMatrixAt(matrix, rowIdx, colIdx) > getMatrixAt(matrix, maxIdx[colIdx], colIdx))
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maxIdx[colIdx] = rowIdx;
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}
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}
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if (matrix.buffer != NULL) {
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for (int i = 0; i < count; i++) {
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for (int j = 0; j < images[i].width * images[i].height; j++) {
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setMatrixAt((MatrixType)images[i].buffer[j], matrix, j, i);
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}
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}
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}
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}
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return maxIdx;
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return matrix;
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}
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unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[], unsigned int numberOfImages)
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{
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Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages);
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Matrix outputBatch = forward(model, inputBatch);
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static Matrix forward(const NeuralNetwork model, Matrix inputBatch) {
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Matrix result = inputBatch;
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unsigned char *result = argmax(outputBatch);
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clearMatrix(&outputBatch);
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return result;
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if (result.buffer != NULL) {
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for (int i = 0; i < model.numberOfLayers; i++) {
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Matrix biasResult;
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Matrix weightResult;
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weightResult = multiply(model.layers[i].weights, result);
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clearMatrix(&result);
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biasResult = add(model.layers[i].biases, weightResult);
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clearMatrix(&weightResult);
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if (model.layers[i].activation != NULL)
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model.layers[i].activation(&biasResult);
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result = biasResult;
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}
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}
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return result;
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}
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void clearModel(NeuralNetwork *model)
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{
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if(model != NULL)
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{
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for(int i = 0; i < model->numberOfLayers; i++)
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{
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clearLayer(&model->layers[i]);
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unsigned char *argmax(const Matrix matrix) {
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unsigned char *maxIdx = NULL;
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if (matrix.rows > 0 && matrix.cols > 0) {
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maxIdx = (unsigned char *)malloc(sizeof(unsigned char) * matrix.cols);
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if (maxIdx != NULL) {
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for (int colIdx = 0; colIdx < matrix.cols; colIdx++) {
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maxIdx[colIdx] = 0;
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for (int rowIdx = 1; rowIdx < matrix.rows; rowIdx++) {
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if (getMatrixAt(matrix, rowIdx, colIdx) >
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getMatrixAt(matrix, maxIdx[colIdx], colIdx))
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maxIdx[colIdx] = rowIdx;
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}
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model->layers = NULL;
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model->numberOfLayers = 0;
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}
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}
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}
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return maxIdx;
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}
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unsigned char *predict(const NeuralNetwork model, const GrayScaleImage images[],
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unsigned int numberOfImages) {
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Matrix inputBatch = imageBatchToMatrixOfImageVectors(images, numberOfImages);
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Matrix outputBatch = forward(model, inputBatch);
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unsigned char *result = argmax(outputBatch);
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clearMatrix(&outputBatch);
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return result;
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}
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void clearModel(NeuralNetwork *model) {
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if (model != NULL) {
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for (int i = 0; i < model->numberOfLayers; i++) {
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clearLayer(&model->layers[i]);
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}
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model->layers = NULL;
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model->numberOfLayers = 0;
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}
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}
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