rawalcher/c-net
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Merge remote-tracking branch 'origin/Development' into Development

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Thomas Schleicher 2023-09-19 15:06:39 +02:00
commit 486ca1ff57
3 changed files with 69 additions and 8 deletions
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2
.gitignore vendored
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@ -54,3 +54,5 @@ modules.order
Module.symvers
Mkfile.old
dkms.conf
/.idea/.name
/.idea/misc.xml

71
neuronal_network.c
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@ -1,11 +1,41 @@
#include <stdlib.h>
#include "neuronal_network.h"
#include <stdio.h>
#include <time.h>
Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate);
void print_network(Neural_Network* network);
void free_network(Neural_Network* network);
Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate){
Neural_Network network = malloc(sizeof(Neural_Network));
// initialize networks variables
network.input_size = input_size;
network.hidden_size = hidden_size;
network.output_size = output_size;
network.learning_rate = learning_rate;
network.weights_1 = matrix_randomize(matrix_create(hidden_size, input_size));
network.weights_2 = matrix_randomize(matrix_create(hidden_size, hidden_size));
network.weights_3 = matrix_randomize(matrix_create(hidden_size, hidden_size));
network.weights_output = matrix_randomize(matrix_create(output_size, hidden_size));
network.bias_1 = matrix_randomize(matrix_create(hidden_size, 1));
network.bias_2 = matrix_randomize(matrix_create(hidden_size, 1));
network.bias_3 = matrix_randomize(matrix_create(hidden_size, 1));
//network.bias_output = matrix_create(output_size, 1); // do we need it?
return network;
}
//void print_network(Neural_Network* network){};
void free_network(Neural_Network* network){
matrix_free(network->weights_1);
matrix_free(network->weights_2);
matrix_free(network->weights_3);
matrix_free(network->weights_output);
matrix_free(network->bias_1);
matrix_free(network->bias_2);
matrix_free(network->bias_3);
free(network);
}
void save_network(Neural_Network* network) {
@ -54,9 +84,38 @@ Neural_Network* load_network(char* file) {
}
double predict_images(Neural_Network* network, Image** images, int amount);
double predict_images(Neural_Network* network, Image** images, int amount) {
int num_correct = 0;
for (int i = 0; i < amount; i++) {
Matrix* prediction = predict_image(network, images[i]);
if (matrix_argmax(prediction) == images[i]->image_label) {
num_correct++;
}
matrix_free(prediction);
}
return 1.0 * num_correct / amount;
}
Matrix* predict_image(Neural_Network* network, Image*);
Matrix* predict(Neural_Network* network, Matrix* image_data);
Matrix* predict(Neural_Network* network, Matrix* image_data) {
Matrix* hidden1_inputs = dot(network->weights_1, image_data);
Matrix* hidden1_outputs = apply(relu, hidden1_inputs);
Matrix* hidden2_inputs = dot(network->weights_2, hidden1_outputs);
Matrix* hidden2_outputs = apply(relu, hidden2_inputs);
Matrix* final_inputs = dot(net->output_weights, hidden_outputs);
Matrix* final_outputs = apply(sigmoid, final_inputs);
Matrix* result = softmax(final_outputs);
matrix_free(hidden_inputs);
matrix_free(hidden_outputs);
matrix_free(final_inputs);
matrix_free(final_outputs);
return result;
}
void train_network(Neural_Network* network, Matrix* input, Matrix* output);
void batch_train_network(Neural_Network* network, Image** images, int size);

2
neuronal_network.h
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@ -26,7 +26,7 @@ typedef struct {
} Neural_Network;
Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate);
void print_network(Neural_Network* network);
//void print_network(Neural_Network* network);
void free_network(Neural_Network* network);
void save_network(Neural_Network* network);