227 lines
No EOL
7.2 KiB
C
227 lines
No EOL
7.2 KiB
C
#include <stdlib.h>
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#include "neuronal_network.h"
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#include <stdio.h>
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#include <math.h>
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double sigmoid(double input);
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Matrix* predict(Neural_Network* network, Matrix* image_data);
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double square(double input);
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Matrix * backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old);
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Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate){
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Neural_Network *network = malloc(sizeof(Neural_Network));
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// initialize networks variables
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network->hidden_size = hidden_size;
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network->input_size = input_size;
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network->output_size = output_size;
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network->learning_rate = learning_rate;
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network->weights_1 = matrix_create(hidden_size, input_size);
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network->weights_2 = matrix_create(hidden_size, hidden_size);
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network->weights_3 = matrix_create(hidden_size, hidden_size);
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network->weights_output = matrix_create(output_size, hidden_size);
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network->bias_1 = matrix_create(hidden_size, 1);
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network->bias_2 = matrix_create(hidden_size, 1);
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network->bias_3 = matrix_create(hidden_size, 1);
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network->bias_output = matrix_create(output_size, 1);
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return network;
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}
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void randomize_network(Neural_Network* network, int scope){
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matrix_randomize(network->weights_1, scope);
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matrix_randomize(network->weights_2, scope);
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matrix_randomize(network->weights_3, scope);
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matrix_randomize(network->weights_output, scope);
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// matrix_randomize(network->bias_1, scope);
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// matrix_randomize(network->bias_2, scope);
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// matrix_randomize(network->bias_3, scope);
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// matrix_randomize(network->bias_output, scope);
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matrix_fill(network->bias_1, 1);
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matrix_fill(network->bias_2, 1);
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matrix_fill(network->bias_3, 1);
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matrix_fill(network->bias_output, 1);
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}
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void free_network(Neural_Network* network){
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matrix_free(network->weights_1);
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matrix_free(network->weights_2);
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matrix_free(network->weights_3);
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matrix_free(network->weights_output);
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matrix_free(network->bias_1);
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matrix_free(network->bias_2);
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matrix_free(network->bias_3);
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matrix_free(network->bias_output);
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free(network);
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}
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void save_network(Neural_Network* network) {
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char* file_name = "../networks/newest_network.txt";
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// create file
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FILE* save_file = fopen(file_name, "w");
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// check if file is successfully opened
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if(save_file == NULL) {
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printf("ERROR: Something went wrong in file creation! (save_network)");
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exit(1);
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}
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// save network size to first line of the file
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fprintf(save_file, "%d\n", network->input_size);
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fprintf(save_file, "%d\n", network->hidden_size);
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fprintf(save_file, "%d\n", network->output_size);
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// close the file
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fclose(save_file);
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// save first layer
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matrix_save(network->bias_1, file_name);
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matrix_save(network->weights_1, file_name);
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// save second layer
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matrix_save(network->bias_2, file_name);
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matrix_save(network->weights_2, file_name);
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// save third layer
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matrix_save(network->bias_3, file_name);
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matrix_save(network->weights_3, file_name);
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// save output weights
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matrix_save(network->bias_output, file_name);
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matrix_save(network->weights_output, file_name);
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printf("Network Saved!");
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}
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Neural_Network* load_network(char* file) {
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// create file pointer and open file
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FILE* save_file = fopen(file, "r");
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// check if file could be opened
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if(save_file == NULL) {
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printf("ERROR: File could not be opened/found! (load_network)");
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exit(1);
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}
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// read & store the information on the size of the network from the save file
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char buffer[MAX_BYTES];
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fgets(buffer, MAX_BYTES, save_file);
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int input_size = (int) strtol(buffer, NULL, 10);
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fgets(buffer, MAX_BYTES, save_file);
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int hidden_size = (int) strtol(buffer, NULL, 10);
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fgets(buffer, MAX_BYTES, save_file);
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int output_size = (int) strtol(buffer, NULL, 10);
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// create a new network to fill with the saved data
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Neural_Network* saved_network = new_network(input_size, hidden_size, output_size, 0);
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// load matrices from file into struct
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saved_network->bias_1 = load_next_matrix(save_file);
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saved_network->weights_1 = load_next_matrix(save_file);
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saved_network->bias_2 = load_next_matrix(save_file);
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saved_network->weights_2 = load_next_matrix(save_file);
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saved_network->bias_3 = load_next_matrix(save_file);
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saved_network->weights_3 = load_next_matrix(save_file);
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saved_network->bias_output = load_next_matrix(save_file);
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saved_network->weights_output = load_next_matrix(save_file);
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// return saved network
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fclose(save_file);
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return saved_network;
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}
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void print_network(Neural_Network* network) {
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matrix_print(network->bias_1);
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matrix_print(network->bias_2);
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matrix_print(network->bias_3);
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matrix_print(network->bias_output);
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matrix_print(network->weights_1);
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matrix_print(network->weights_2);
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matrix_print(network->weights_3);
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matrix_print(network->weights_output);
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}
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double measure_network_accuracy(Neural_Network* network, Image** images, int amount) {
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int num_correct = 0;
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for (int i = 0; i < amount; i++) {
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Matrix* prediction = predict_image(network, images[i]);
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if (matrix_argmax(prediction) == images[i]->label) {
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num_correct++;
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}
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matrix_free(prediction);
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}
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return ((double) num_correct) / amount;
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}
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Matrix* predict_image(Neural_Network* network, Image* image){
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Matrix* image_data = matrix_flatten(image->pixel_values, 0);
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Matrix* res = predict(network, image_data);
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matrix_free(image_data);
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return res;
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}
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Matrix* predict(Neural_Network* network, Matrix* image_data) {
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Matrix* h1_dot = dot(network->weights_1, image_data);
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Matrix* h1_add = add(h1_dot, network->bias_1);
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Matrix* h1_outputs = apply(sigmoid, h1_add);
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Matrix* h2_dot = dot(network->weights_2, h1_outputs);
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Matrix* h2_add = add(h2_dot, network->bias_2);
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Matrix* h2_outputs = apply(sigmoid, h2_add);
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Matrix* h3_dot = dot(network->weights_3, h2_outputs);
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Matrix* h3_add = add(h3_dot, network->bias_3);
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Matrix* h3_outputs = apply(sigmoid, h3_add);
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Matrix* final_dot = dot(network->weights_output, h3_outputs);
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Matrix* final_add = add(final_dot, network->bias_output);
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Matrix* final_outputs = apply(sigmoid, final_add);
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matrix_free(h1_dot);
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matrix_free(h1_add);
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matrix_free(h1_outputs);
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matrix_free(h2_dot);
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matrix_free(h2_add);
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matrix_free(h2_outputs);
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matrix_free(h3_dot);
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matrix_free(h3_add);
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matrix_free(h3_outputs);
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matrix_free(final_dot);
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matrix_free(final_add);
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return final_outputs;
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}
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void train_network(Neural_Network* network, Image *image, int label) {
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Matrix* input = matrix_flatten(image->pixel_values, 0);
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// Forward Pass
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Matrix* h1_dot = dot(network->weights_1, input);
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Matrix* h1_add = add()
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matrix_free(input);
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}
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Matrix * backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old) {
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return NULL;
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}
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double sigmoid(double input) {
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return 1.0 / (1 + exp(-1 * input));
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}
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double square(double input) {
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return input * input;
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} |