121 lines
No EOL
3.8 KiB
C
121 lines
No EOL
3.8 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 <time.h>
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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.input_size = input_size;
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network.hidden_size = hidden_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_randomize(matrix_create(hidden_size, input_size));
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network.weights_2 = matrix_randomize(matrix_create(hidden_size, hidden_size));
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network.weights_3 = matrix_randomize(matrix_create(hidden_size, hidden_size));
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network.weights_output = matrix_randomize(matrix_create(output_size, hidden_size));
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network.bias_1 = matrix_randomize(matrix_create(hidden_size, 1));
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network.bias_2 = matrix_randomize(matrix_create(hidden_size, 1));
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network.bias_3 = matrix_randomize(matrix_create(hidden_size, 1));
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//network.bias_output = matrix_create(output_size, 1); // do we need it?
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return network;
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}
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//void print_network(Neural_Network* network){};
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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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free(network);
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}
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void save_network(Neural_Network* network) {
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// create file name and file string
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time_t seconds;
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time(&seconds);
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char* file_name = "../networks/";
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sprintf(file_name, "%ld", seconds);
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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(file_name);
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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->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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}
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double predict_images(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]->image_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 1.0 * num_correct / amount;
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}
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Matrix* predict_image(Neural_Network* network, Image*);
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Matrix* predict(Neural_Network* network, Matrix* image_data) {
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Matrix* hidden1_inputs = dot(network->weights_1, image_data);
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Matrix* hidden1_outputs = apply(relu, hidden1_inputs);
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Matrix* hidden2_inputs = dot(network->weights_2, hidden1_outputs);
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Matrix* hidden2_outputs = apply(relu, hidden2_inputs);
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Matrix* final_inputs = dot(net->output_weights, hidden_outputs);
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Matrix* final_outputs = apply(sigmoid, final_inputs);
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Matrix* result = softmax(final_outputs);
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matrix_free(hidden_inputs);
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matrix_free(hidden_outputs);
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matrix_free(final_inputs);
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matrix_free(final_outputs);
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return result;
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}
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void train_network(Neural_Network* network, Matrix* input, Matrix* output);
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void batch_train_network(Neural_Network* network, Image** images, int size); |