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minor changes

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Thomas 2023-09-22 12:54:04 +02:00
parent 0e2c972d0f
commit 3349ce7a43
4 changed files with 68 additions and 102 deletions
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147
neuronal_network.c
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@ -1,17 +1,11 @@
#include <stdlib.h>
#include "neuronal_network.h"
#include <stdio.h>
#include <time.h>
#include <math.h>
double sigmoid(double input);
double sigmoid_derivative(double x);
Matrix* softmax(Matrix* matrix);
Matrix* predict(Neural_Network* network, Matrix* image_data);
double square(double input);
double loss_function(Matrix* output_matrix, int image_label);
Matrix * 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){
@ -135,6 +129,18 @@ Neural_Network* load_network(char* file) {
return saved_network;
}
void print_network(Neural_Network* network) {
matrix_print(network->bias_1);
matrix_print(network->bias_2);
matrix_print(network->bias_3);
matrix_print(network->bias_output);
matrix_print(network->weights_1);
matrix_print(network->weights_2);
matrix_print(network->weights_3);
matrix_print(network->weights_output);
}
double measure_network_accuracy(Neural_Network* network, Image** images, int amount) {
int num_correct = 0;
for (int i = 0; i < amount; i++) {
@ -144,7 +150,7 @@ double measure_network_accuracy(Neural_Network* network, Image** images, int amo
}
matrix_free(prediction);
}
return 1.0 * num_correct / amount;
return ((double) num_correct) / amount;
}
Matrix* predict_image(Neural_Network* network, Image* image){
@ -171,8 +177,6 @@ Matrix* predict(Neural_Network* network, Matrix* image_data) {
Matrix* final_add = add(final_dot, network->bias_output);
Matrix* final_outputs = apply(sigmoid, final_add);
Matrix* result = softmax(final_outputs);
matrix_free(h1_dot);
matrix_free(h1_add);
matrix_free(h1_outputs);
@ -187,18 +191,8 @@ Matrix* predict(Neural_Network* network, Matrix* image_data) {
matrix_free(final_dot);
matrix_free(final_add);
matrix_free(final_outputs);
return result;
}
double cost_function(Matrix* calculated, int expected){
calculated->numbers[expected] -= 1;
apply(square, calculated);
// double loss = 0.5 * (target - output) * (target - output);
return 0;
return final_outputs;
}
void train_network(Neural_Network* network, Image *image, int label) {
@ -224,42 +218,45 @@ void train_network(Neural_Network* network, Image *image, int label) {
Matrix* final_outputs = apply(sigmoid, final_add);
// begin backpropagation
Matrix* temp9 = matrix_create(final_outputs->rows, 1);
matrix_fill(temp9, 1);
Matrix* temp1 = subtract(temp9, final_outputs);
Matrix* temp2 = multiply(temp1, final_outputs); // * soll-ist
Matrix* temp3 = matrix_create(final_outputs->rows, final_outputs->columns);
matrix_fill(temp3, 0);
temp3->numbers[label][0] = 1;
Matrix* temp4 = subtract(temp3, final_outputs);
Matrix* sigma1 = multiply(temp2, temp4);
// The output of this is equal to an array of the size (10, 1) where each element is the derivative of the sigmoid function
// with the input of the neuron prior to the application of the activation function
Matrix* matrix_filled_with_ones = matrix_create(final_outputs->rows, 1);
matrix_fill(matrix_filled_with_ones, 1);
Matrix* temp1 = subtract(matrix_filled_with_ones, final_outputs);
Matrix* derivative_input = multiply(final_outputs, temp1); // * soll-ist
// create label matrix, which indicates the correct output of the neural network
Matrix* correct_output = matrix_create(final_outputs->rows, final_outputs->columns);
matrix_fill(correct_output, 0);
correct_output->numbers[label][0] = 1;
// calculate the difference between what the value should be and what it actually is (MAYBE USE MES)
Matrix* error_difference = subtract(final_outputs, correct_output); // * output ist minus output soll
// multiply the derivative of the activation function with the input to the neuron
Matrix* sigma1 = multiply(derivative_input, error_difference);
// Calculate the delta for the weights
Matrix* temp5 = transpose(h3_outputs);
Matrix* temp6 = dot(sigma1, temp5);
Matrix* weights_delta = scale(temp6, network->learning_rate);
Matrix* bias_delta = scale(sigma1, network->learning_rate);
// Matrix* temp7 = add(weights_delta, network->weights_output);
// matrix_free(network->weights_output);
// network->weights_output = temp7;
//
// Matrix* temp8 = add(bias_delta, network->bias_output);
// matrix_free(network->bias_output);
// network->bias_output = temp8;
Matrix* temp7 = add(weights_delta, network->weights_output);
Matrix* temp7 = add(network->weights_output, weights_delta);
for (int i = 0; i < network->weights_output->rows; ++i) {
for (int j = 0; j < network->weights_output->columns; ++j) {
network->weights_output->numbers[i][j] = temp7->numbers[i][j];
}
}
Matrix* temp8 = add(bias_delta, network->bias_output);
for (int i = 0; i < network->bias_output->rows; ++i) {
for (int j = 0; j < network->bias_output->columns; ++j) {
network->bias_output->numbers[i][j] = temp8->numbers[i][j];
}
}
// Matrix* temp8 = add(network->bias_output, bias_delta);
// for (int i = 0; i < network->bias_output->rows; ++i) {
// for (int j = 0; j < network->bias_output->columns; ++j) {
// network->bias_output->numbers[i][j] = temp8->numbers[i][j];
// }
// }
// other levels
Matrix* sigma2 = backPropagation(network->learning_rate, network->weights_3, network->bias_3, h3_outputs, h2_outputs, sigma1);
@ -294,14 +291,14 @@ void train_network(Neural_Network* network, Image *image, int label) {
matrix_free(temp1);
matrix_free(temp2);
matrix_free(temp3);
matrix_free(temp4);
matrix_free(derivative_input);
matrix_free(correct_output);
matrix_free(error_difference);
matrix_free(temp5);
matrix_free(temp6);
matrix_free(temp7);
matrix_free(temp8);
matrix_free(temp9);
// matrix_free(temp8);
matrix_free(matrix_filled_with_ones);
}
Matrix * backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old) {
@ -327,26 +324,26 @@ Matrix * backPropagation(double learning_rate, Matrix* weights, Matrix* biases,
Matrix* weights_delta = scale(temp4, learning_rate);
Matrix* bias_delta = scale(sigma_new, learning_rate);
Matrix* temp5 = add(weights_delta, weights);
Matrix* temp5 = add(weights, weights_delta);
for (int i = 0; i < weights->rows; ++i) {
for (int j = 0; j < weights->columns; ++j) {
weights->numbers[i][j] = temp5->numbers[i][j];
}
}
Matrix* temp6 = add(bias_delta, biases);
for (int i = 0; i < biases->rows; ++i) {
for (int j = 0; j < biases->columns; ++j) {
biases->numbers[i][j] = temp6->numbers[i][j];
}
}
// Matrix* temp6 = add(biases, bias_delta);
// for (int i = 0; i < biases->rows; ++i) {
// for (int j = 0; j < biases->columns; ++j) {
// biases->numbers[i][j] = temp6->numbers[i][j];
// }
// }
matrix_free(temp1);
matrix_free(temp2);
matrix_free(temp3);
matrix_free(temp4);
matrix_free(temp5);
matrix_free(temp6);
// matrix_free(temp6);
matrix_free(temp7);
matrix_free(weights_delta);
matrix_free(bias_delta);
@ -358,38 +355,6 @@ double sigmoid(double input) {
return 1.0 / (1 + exp(-1 * input));
}
double sigmoid_derivative(double x) {
return x * (1.0 - x);
}
Matrix* softmax(Matrix* matrix) {
double total = 0;
for (int i = 0; i < matrix->rows; i++) {
for (int j = 0; j < matrix->columns; j++) {
total += exp(matrix->numbers[i][j]);
}
}
Matrix* result_matrix = matrix_create(matrix->rows, matrix->columns);
for (int i = 0; i < result_matrix->rows; i++) {
for (int j = 0; j < result_matrix->columns; j++) {
result_matrix->numbers[i][j] = exp(matrix->numbers[i][j]) / total;
}
}
return result_matrix;
}
double square(double input) {
return input * input;
}
//double loss_function(Matrix* output_matrix, int image_label) {
// Matrix* temp = matrix_copy(output_matrix);
//
// temp->numbers[1][image_label] -= 1;
// apply(square, temp);
//
// matrix_free(temp);
//
// return matrix_sum(temp);;
//}
}