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Merge branch 'Demonstration' into 'main'

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!(feat) cli implemented

See merge request jastornig/c-net!9
This commit is contained in:
jastornig 2023-09-24 18:59:58 +00:00
commit 61339556c7
3 changed files with 116 additions and 20 deletions
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124
main.c
View file

@ -2,28 +2,122 @@
#include "image.h"
#include "neuronal_network.h"
#include <stdlib.h>
#include <string.h>
#include <errno.h>
#include "util.h"
int main() {
Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, 60000);
// img_visualize(images[0]);
// img_visualize(images[1]);
void parsingErrorPrintHelp(){
printf("Syntax: c_net [train | detect]\n");
printf("commands:\n");
printf("train\t train the network\n");
printf("predict\t load a pgm image and predict_demo the number\n");
exit(1);
}
// matrix_print(images[0]->pixel_values);
// matrix_print(images[1]->pixel_values);
void parsingErrorTrain(){
printf("invalid syntax\n");
printf("Syntax: c_net train [path_to_train-images.idx3-ubyte] [path_to_train-labels.idx1-ubyte] [hidden_layer_count] [neurons_per_layer] [epochs] [learning_rate] [path_to_save_network]\n");
exit(1);
}
Neural_Network* nn = new_network(28*28, 40, 5, 10, 0.08);
randomize_network(nn, 1);
// Neural_Network* nn = load_network("../networks/newest_network.txt");
// printf("Done loading!\n");
void parsingErrorDetect(){
printf("invalid syntax\n");
printf("Syntax: c_net predict_demo [path_to_network] [image_file]");
}
// batch_train(nn, images, 20000, 20);
void predict_demo(int argc, char** arguments){
if(argc != 2) parsingErrorDetect();
char * network_file = arguments[0];
char * image_file = arguments[1];
for (int i = 0; i < 30000; ++i) {
train_network(nn, images[i], images[i]->label);
Neural_Network * nn = load_network(network_file);
Image * image = load_pgm_image(image_file);
Matrix * result = predict_image(nn, image);
int predicted = matrix_argmax(result);
printf("prediction result %d\n", predicted);
matrix_print(result);
matrix_free(result);
}
void train(int argc, char** arguments) {
if (argc != 7) parsingErrorTrain();
char *image_file = arguments[0];
char *label_file = arguments[1];
int hidden_count = (int) strtol(arguments[2], NULL, 10);
int neurons_per_layer = (int) strtol(arguments[3], NULL, 10);
int epochs = (int) strtol(arguments[4], NULL, 10);
if (errno != 0) {
printf("hidden_count, neurons_per_layer or epochs could not be parsed!\n");
exit(1);
}
double learning_rate = strtod(arguments[5], NULL);
if (errno != 0) {
printf("learning_rate could not be parsed!\n");
exit(1);
}
char *save_path = arguments[6];
int imported = 0;
Image **images = import_images(image_file, label_file, &imported, 50000);
save_network(nn);
// for(int i = 0; i < imported; i++){
// matrix_save(images[i]->pixel_values, "images.txt");
// }
// exit(1);
printf("%lf\n", measure_network_accuracy(nn, images, 10000));
Neural_Network *nn = new_network(28 * 28, neurons_per_layer, hidden_count, 10, learning_rate);
randomize_network(nn, 1);
printf("training_network\n");
for(int epoch = 1; epoch <= epochs; epoch++){
printf("epoch %d\n", epoch);
for (int i = 0; i < imported; i++) {
if (i % 1000 == 0) {
updateBar(i * 100 / imported);
}
train_network(nn, images[i], images[i]->label);
}
updateBar(100);
printf("\n");
printf("accuracy %lf\n", measure_network_accuracy(nn, images, 10000));
}
printf("done training!\n");
save_network(nn, save_path);
}
int main(int argc, char** argv) {
// Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, 60000);
//// img_visualize(images[0]);
//// img_visualize(images[1]);
//
//// matrix_print(images[0]->pixel_values);
//// matrix_print(images[1]->pixel_values);
//
// Neural_Network* nn = new_network(28*28, 40, 5, 10, 0.08);
// randomize_network(nn, 1);
//// Neural_Network* nn = load_network("../networks/newest_network.txt");
//// printf("Done loading!\n");
//
//// batch_train(nn, images, 20000, 20);
//
// for (int i = 0; i < 30000; ++i) {
// train_network(nn, images[i], images[i]->label);
// }
//
// save_network(nn);
//
// printf("%lf\n", measure_network_accuracy(nn, images, 10000));
if(argc < 2){
parsingErrorPrintHelp();
exit(1);
}
if(strcmp(argv[1], "train") == 0){
train(argc-2, argv+2);
return 0;
}
if(strcmp(argv[1], "predict") == 0){
predict_demo(argc - 2, argv + 2);
return 0;
}
parsingErrorPrintHelp();
}

10
neuronal_network.c
View file

@ -2,6 +2,7 @@
#include "neuronal_network.h"
#include <stdio.h>
#include <math.h>
#include "util.h"
double sigmoid(double input);
Matrix* predict(Neural_Network* network, Matrix* image_data);
@ -45,9 +46,7 @@ void free_network(Neural_Network* network){
free(network);
}
void save_network(Neural_Network* network) {
char* file_name = "../networks/newest_network.txt";
void save_network(Neural_Network* network, char * file_name) {
// create file
FILE* save_file = fopen(file_name, "w");
@ -117,7 +116,9 @@ void print_network(Neural_Network* network) {
double measure_network_accuracy(Neural_Network* network, Image** images, int amount) {
int num_correct = 0;
for (int i = 0; i < amount; i++) {
printf("evaluating network\n");
for (int i = 50001; i < amount; i++) {
updateBar(i*100/amount);
Matrix* prediction = predict_image(network, images[i]);
int guess = matrix_argmax(prediction);
@ -129,6 +130,7 @@ double measure_network_accuracy(Neural_Network* network, Image** images, int amo
matrix_free(prediction);
}
updateBar(100);
return ((double) num_correct) / amount;
}

2
neuronal_network.h
View file

@ -21,7 +21,7 @@ Neural_Network* new_network(int input_size, int hidden_size, int hidden_amount,
void randomize_network(Neural_Network* network, int scope);
void free_network(Neural_Network* network);
void save_network(Neural_Network* network);
void save_network(Neural_Network* network, char * file_name);
Neural_Network* load_network(char* file);
void print_network(Neural_Network* network);