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HolyFuckItsAlive #13

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jastornig merged 105 commits from Delta-Error-Test into main 2023-09-23 22:27:54 +02:00
Conversation 3 Commits 105 Files changed 19 +37 -19
3 changed files with 37 additions and 19 deletions
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8
main.c
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@ -5,13 +5,15 @@
#include "neuronal_network.h"
int main() {
// Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, 60000);
Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, 60000);
// img_visualize(images[4]);
// Neural_Network* nn = new_network(4, 2, 3, 0.5);
// randomize_network(nn, 20);
Neural_Network* nn = new_network(28*28, 16, 10, 0.5);
randomize_network(nn, 20);
// save_network(nn);
// Neural_Network* nn = load_network("../networks/test1.txt");
train_network(nn, images[0], 5);
}

46
neuronal_network.c
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@ -4,9 +4,12 @@
#include <time.h>
#include <math.h>
double relu(double input);
Matrix* softmax(Matrix* matrix);
double sigmoid(double input);
Matrix* sigmoidPrime(Matrix* m);
Matrix* softmax(Matrix* matrix);
double square(double input);
double loss_function(Matrix* output_matrix, int image_label);
Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate){
@ -152,13 +155,13 @@ Matrix* predict_image(Neural_Network* network, Image* image){
}
Matrix* predict(Neural_Network* network, Matrix* image_data) {
Matrix* hidden1_outputs = apply(relu, add(dot(network->weights_1, image_data), network->bias_1));
Matrix* hidden1_outputs = apply(sigmoid, add(dot(network->weights_1, image_data), network->bias_1));
Matrix* hidden2_outputs = apply(relu, add(dot(network->weights_2, hidden1_outputs), network->bias_2));
Matrix* hidden2_outputs = apply(sigmoid, add(dot(network->weights_2, hidden1_outputs), network->bias_2));
Matrix* hidden3_outputs = apply(relu, add(dot(network->weights_3, hidden2_outputs), network->bias_3));
Matrix* hidden3_outputs = apply(sigmoid, add(dot(network->weights_3, hidden2_outputs), network->bias_3));
Matrix* final_outputs = apply(relu, add(dot(network->weights_output, hidden3_outputs), network->bias_output));
Matrix* final_outputs = apply(sigmoid, add(dot(network->weights_output, hidden3_outputs), network->bias_output));
Matrix* result = softmax(final_outputs);
@ -176,18 +179,31 @@ double cost_function(Matrix* calculated, int expected){
}
//void train_network(Neural_Network* network, Matrix* input, Matrix* output);
//void batch_train_network(Neural_Network* network, Image** images, int size);
void train_network(Neural_Network* network, Image *image, int label) {
Matrix* input = matrix_flatten(image->pixel_values, 0);
Matrix* hidden1_outputs = apply(sigmoid, add(dot(network->weights_1, input), network->bias_1));
Matrix* hidden2_outputs = apply(sigmoid, add(dot(network->weights_2, hidden1_outputs), network->bias_2));
Matrix* hidden3_outputs = apply(sigmoid, add(dot(network->weights_3, hidden2_outputs), network->bias_3));
Matrix* final_outputs = apply(sigmoid, add(dot(network->weights_output, hidden3_outputs), network->bias_output));
double relu(double input) {
if (input <= 0){
return 0.0;
}
return input;
}
double relu_derivative(double x) {
return (x > 0) ? 1 : 0;
//void batch_train_network(Neural_Network* network, Image** images, int size);
double sigmoid(double input) {
return 1.0 / (1 + exp(-1 * input));
}
Matrix* sigmoidPrime(Matrix* m) {
Matrix* ones = matrix_create(m->rows, m->columns);
matrix_fill(ones, 1);
Matrix* subtracted = subtract(ones, m);
Matrix* multiplied = multiply(m, subtracted);
matrix_free(ones);
matrix_free(subtracted);
return multiplied;
}
Matrix* softmax(Matrix* matrix) {

2
neuronal_network.h
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@ -39,5 +39,5 @@ double measure_network_accuracy(Neural_Network* network, Image** images, int amo
Matrix* predict_image(Neural_Network* network, Image* image);
Matrix* predict(Neural_Network* network, Matrix* image_data);
void train_network(Neural_Network* network, Matrix* input, Matrix* output);
void train_network(Neural_Network* network, Image *image, int label);
void batch_train_network(Neural_Network* network, Image** images, int size);