rawalcher/c-net
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Thomas Schleicher 2023-09-21 18:11:19 +02:00
parent 64ad4aefe4
commit 9ca185c30f
2 changed files with 68 additions and 51 deletions
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9
main.c
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@ -14,6 +14,13 @@ int main() {
// Neural_Network* nn = load_network("../networks/test1.txt"); // Neural_Network* nn = load_network("../networks/test1.txt");
train_network(nn, images[0], 5);
for (int i = 0; i < 10000; ++i) {
train_network(nn, images[i], images[i]->label);
}
measure_network_accuracy(nn, images, 100);
} }

110
neuronal_network.c
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@ -5,13 +5,15 @@
#include <math.h> #include <math.h>
double sigmoid(double input); double sigmoid(double input);
Matrix* sigmoidPrime(Matrix* m); double sigmoid_derivative(double x);
Matrix* softmax(Matrix* matrix); Matrix* softmax(Matrix* matrix);
double square(double input); double square(double input);
double loss_function(Matrix* output_matrix, int image_label); double loss_function(Matrix* output_matrix, int image_label);
void backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old);
Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate){ Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate){
Neural_Network *network = malloc(sizeof(Neural_Network)); Neural_Network *network = malloc(sizeof(Neural_Network));
// initialize networks variables // initialize networks variables
@ -196,6 +198,9 @@ double cost_function(Matrix* calculated, int expected){
calculated->numbers[expected] -= 1; calculated->numbers[expected] -= 1;
apply(square, calculated); apply(square, calculated);
// double loss = 0.5 * (target - output) * (target - output);
return 0;
} }
void train_network(Neural_Network* network, Image *image, int label) { void train_network(Neural_Network* network, Image *image, int label) {
@ -243,68 +248,73 @@ void train_network(Neural_Network* network, Image *image, int label) {
// other levels // other levels
Matrix* sigma_current = matrix_create(hidden3_outputs->rows, 1); backPropagation(network->learning_rate, network->weights_3, network->bias_3, hidden3_outputs, hidden2_outputs, sigma);
matrix_fill(sigma_current, 1); backPropagation(network->learning_rate, network->weights_2, network->bias_2, hidden2_outputs, hidden1_outputs, sigma);
temp_1 = subtract(sigma_current, hidden3_outputs); backPropagation(network->learning_rate, network->weights_1, network->bias_1, hidden1_outputs, input, sigma);
temp_2 = multiply(temp_1, hidden3_outputs); // *sum(delta*weights)
for(int j=0;j<hidden3_outputs->rows;j++) {
double sum = 0;
for (int i = 0; i < sigma->rows; i++) {
sum += hidden3_outputs->numbers[j][i]*sigma->numbers[i][0];
}
temp_1->numbers[j][0]=sum;
}
sigma_current = multiply(temp_2, temp_1);
// sigma done
temp1 = transpose(hidden2_outputs);
temp2 = dot(sigma_current, temp1);
weights_delta = scale(temp2, network->learning_rate);
bias_delta = scale(sigma_current, network->learning_rate);
temp = add(weights_delta, network->weights_3);
matrix_free(network->weights_3);
network->weights_3 = temp;
temp = add(bias_delta, network->bias_3);
matrix_free(network->bias_3);
network->bias_3 = temp;
matrix_free(weights_delta);
matrix_free(bias_delta);
matrix_free(temp);
matrix_free(sigma);
matrix_free(temp_1); matrix_free(temp_1);
matrix_free(temp_2); matrix_free(temp_2);
matrix_free(temp1); matrix_free(temp1);
matrix_free(temp2); matrix_free(temp2);
matrix_free(input);
//matrix_print(sigma); matrix_free(hidden1_outputs);
matrix_free(hidden2_outputs);
matrix_free(hidden3_outputs);
matrix_free(final_outputs);
} }
void backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old) {
Matrix* sigma_new = matrix_create(current_layer_activation->rows, 1);
matrix_fill(sigma_new, 1);
Matrix* temp1 = subtract(sigma_new, current_layer_activation);
Matrix* temp2 = multiply(temp1, current_layer_activation); // *sum(delta*weights)
for(int i = 0; i < current_layer_activation->rows; i++) {
double sum = 0;
for (int j = 0; j < sigma_old->rows; j++) {
sum += current_layer_activation->numbers[i][j] * sigma_old->numbers[j][0];
}
temp1->numbers[i][0] = sum;
}
sigma_new = multiply(temp2, temp1);
// new sigma done
temp1 = transpose(previous_layer_activation);
temp2 = dot(sigma_new, temp1);
Matrix* weights_delta = scale(temp2, learning_rate);
Matrix* bias_delta = scale(sigma_new, learning_rate);
temp1 = add(weights_delta, weights);
matrix_free(weights);
weights = temp1;
temp1 = add(bias_delta, biases);
matrix_free(biases);
biases = temp1;
sigma_old = sigma_new;
matrix_free(sigma_new);
matrix_free(temp1);
matrix_free(temp2);
matrix_free(weights_delta);
matrix_free(bias_delta);
}
//void batch_train_network(Neural_Network* network, Image** images, int size); //void batch_train_network(Neural_Network* network, Image** images, int size);
double sigmoid(double input) { double sigmoid(double input) {
return 1.0 / (1 + exp(-1 * input)); return 1.0 / (1 + exp(-1 * input));
} }
Matrix* sigmoidPrime(Matrix* m) { double sigmoid_derivative(double x) {
Matrix* ones = matrix_create(m->rows, m->columns); return x * (1.0 - x);
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) { Matrix* softmax(Matrix* matrix) {