minor changes
This commit is contained in:
Thomas
parent
0e2c972d0f
commit
3349ce7a43
4 changed files with 68 additions and 102 deletions
12
main.c
12
main.c
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@ -1,6 +1,5 @@
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#include <stdio.h>
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#include "matrix.h"
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#include "image.h"
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#include "neuronal_network.h"
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@ -8,17 +7,20 @@ int main() {
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Image** images = import_images("../data/train-images.idx3-ubyte", "../data/train-labels.idx1-ubyte", NULL, 60000);
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// img_visualize(images[4]);
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Neural_Network* nn = new_network(28*28, 100, 10, 0.5);
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randomize_network(nn, 20);
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Neural_Network* nn = new_network(28*28, 50, 10, 0.1);
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randomize_network(nn, 1);
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// save_network(nn);
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// Neural_Network* nn = load_network("../networks/test1.txt");
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for (int i = 0; i < 10000; ++i) {
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for (int i = 0; i < 20000; ++i) {
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train_network(nn, images[i], images[i]->label);
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}
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printf("%lf\n", measure_network_accuracy(nn, images, 100));
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// train_network(nn, images[0], images[0]->label);
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// train_network(nn, images[0], images[0]->label);
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printf("%lf\n", measure_network_accuracy(nn, images, 2000));
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}
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2
matrix.c
2
matrix.c
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@ -349,8 +349,10 @@ int matrix_argmax(Matrix* matrix) {
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printf("ERROR: Matrix is not Mx1 (matrix_argmax)");
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exit(EXIT_FAILURE);
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}
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double max_value = 0;
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int max_index = 0;
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for (int i = 0; i < matrix->rows; i++) {
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if (matrix->numbers[i][0] > max_value) {
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max_value = matrix->numbers[i][0];
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@ -1,17 +1,11 @@
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#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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#include <math.h>
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double sigmoid(double input);
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double sigmoid_derivative(double x);
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Matrix* softmax(Matrix* matrix);
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Matrix* predict(Neural_Network* network, Matrix* image_data);
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double square(double input);
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double loss_function(Matrix* output_matrix, int image_label);
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Matrix * backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old);
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Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate){
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@ -135,6 +129,18 @@ Neural_Network* load_network(char* file) {
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return saved_network;
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}
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void print_network(Neural_Network* network) {
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matrix_print(network->bias_1);
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matrix_print(network->bias_2);
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matrix_print(network->bias_3);
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matrix_print(network->bias_output);
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matrix_print(network->weights_1);
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matrix_print(network->weights_2);
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matrix_print(network->weights_3);
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matrix_print(network->weights_output);
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}
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double measure_network_accuracy(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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@ -144,7 +150,7 @@ double measure_network_accuracy(Neural_Network* network, Image** images, int amo
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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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return ((double) num_correct) / amount;
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}
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Matrix* predict_image(Neural_Network* network, Image* image){
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@ -171,8 +177,6 @@ Matrix* predict(Neural_Network* network, Matrix* image_data) {
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Matrix* final_add = add(final_dot, network->bias_output);
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Matrix* final_outputs = apply(sigmoid, final_add);
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Matrix* result = softmax(final_outputs);
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matrix_free(h1_dot);
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matrix_free(h1_add);
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matrix_free(h1_outputs);
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@ -187,18 +191,8 @@ Matrix* predict(Neural_Network* network, Matrix* image_data) {
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matrix_free(final_dot);
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matrix_free(final_add);
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matrix_free(final_outputs);
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return result;
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}
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double cost_function(Matrix* calculated, int expected){
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calculated->numbers[expected] -= 1;
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apply(square, calculated);
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// double loss = 0.5 * (target - output) * (target - output);
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return 0;
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return final_outputs;
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}
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void train_network(Neural_Network* network, Image *image, int label) {
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@ -224,42 +218,45 @@ void train_network(Neural_Network* network, Image *image, int label) {
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Matrix* final_outputs = apply(sigmoid, final_add);
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// begin backpropagation
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Matrix* temp9 = matrix_create(final_outputs->rows, 1);
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matrix_fill(temp9, 1);
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Matrix* temp1 = subtract(temp9, final_outputs);
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Matrix* temp2 = multiply(temp1, final_outputs); // * soll-ist
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Matrix* temp3 = matrix_create(final_outputs->rows, final_outputs->columns);
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matrix_fill(temp3, 0);
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temp3->numbers[label][0] = 1;
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Matrix* temp4 = subtract(temp3, final_outputs);
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Matrix* sigma1 = multiply(temp2, temp4);
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// The output of this is equal to an array of the size (10, 1) where each element is the derivative of the sigmoid function
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// with the input of the neuron prior to the application of the activation function
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Matrix* matrix_filled_with_ones = matrix_create(final_outputs->rows, 1);
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matrix_fill(matrix_filled_with_ones, 1);
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Matrix* temp1 = subtract(matrix_filled_with_ones, final_outputs);
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Matrix* derivative_input = multiply(final_outputs, temp1); // * soll-ist
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// create label matrix, which indicates the correct output of the neural network
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Matrix* correct_output = matrix_create(final_outputs->rows, final_outputs->columns);
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matrix_fill(correct_output, 0);
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correct_output->numbers[label][0] = 1;
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// calculate the difference between what the value should be and what it actually is (MAYBE USE MES)
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Matrix* error_difference = subtract(final_outputs, correct_output); // * output ist minus output soll
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// multiply the derivative of the activation function with the input to the neuron
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Matrix* sigma1 = multiply(derivative_input, error_difference);
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// Calculate the delta for the weights
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Matrix* temp5 = transpose(h3_outputs);
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Matrix* temp6 = dot(sigma1, temp5);
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Matrix* weights_delta = scale(temp6, network->learning_rate);
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Matrix* bias_delta = scale(sigma1, network->learning_rate);
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// Matrix* temp7 = add(weights_delta, network->weights_output);
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// matrix_free(network->weights_output);
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// network->weights_output = temp7;
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//
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// Matrix* temp8 = add(bias_delta, network->bias_output);
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// matrix_free(network->bias_output);
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// network->bias_output = temp8;
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Matrix* temp7 = add(weights_delta, network->weights_output);
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Matrix* temp7 = add(network->weights_output, weights_delta);
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for (int i = 0; i < network->weights_output->rows; ++i) {
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for (int j = 0; j < network->weights_output->columns; ++j) {
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network->weights_output->numbers[i][j] = temp7->numbers[i][j];
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}
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}
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Matrix* temp8 = add(bias_delta, network->bias_output);
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for (int i = 0; i < network->bias_output->rows; ++i) {
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for (int j = 0; j < network->bias_output->columns; ++j) {
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network->bias_output->numbers[i][j] = temp8->numbers[i][j];
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}
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}
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// Matrix* temp8 = add(network->bias_output, bias_delta);
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// for (int i = 0; i < network->bias_output->rows; ++i) {
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// for (int j = 0; j < network->bias_output->columns; ++j) {
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// network->bias_output->numbers[i][j] = temp8->numbers[i][j];
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// }
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// }
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// other levels
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Matrix* sigma2 = backPropagation(network->learning_rate, network->weights_3, network->bias_3, h3_outputs, h2_outputs, sigma1);
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@ -294,14 +291,14 @@ void train_network(Neural_Network* network, Image *image, int label) {
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matrix_free(temp1);
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matrix_free(temp2);
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matrix_free(temp3);
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matrix_free(temp4);
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matrix_free(derivative_input);
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matrix_free(correct_output);
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matrix_free(error_difference);
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matrix_free(temp5);
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matrix_free(temp6);
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matrix_free(temp7);
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matrix_free(temp8);
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matrix_free(temp9);
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// matrix_free(temp8);
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matrix_free(matrix_filled_with_ones);
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}
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Matrix * backPropagation(double learning_rate, Matrix* weights, Matrix* biases, Matrix* current_layer_activation, Matrix* previous_layer_activation, Matrix* sigma_old) {
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@ -327,26 +324,26 @@ Matrix * backPropagation(double learning_rate, Matrix* weights, Matrix* biases,
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Matrix* weights_delta = scale(temp4, learning_rate);
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Matrix* bias_delta = scale(sigma_new, learning_rate);
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Matrix* temp5 = add(weights_delta, weights);
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Matrix* temp5 = add(weights, weights_delta);
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for (int i = 0; i < weights->rows; ++i) {
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for (int j = 0; j < weights->columns; ++j) {
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weights->numbers[i][j] = temp5->numbers[i][j];
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}
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}
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Matrix* temp6 = add(bias_delta, biases);
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for (int i = 0; i < biases->rows; ++i) {
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for (int j = 0; j < biases->columns; ++j) {
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biases->numbers[i][j] = temp6->numbers[i][j];
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}
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}
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// Matrix* temp6 = add(biases, bias_delta);
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// for (int i = 0; i < biases->rows; ++i) {
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// for (int j = 0; j < biases->columns; ++j) {
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// biases->numbers[i][j] = temp6->numbers[i][j];
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// }
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// }
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matrix_free(temp1);
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matrix_free(temp2);
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matrix_free(temp3);
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matrix_free(temp4);
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matrix_free(temp5);
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matrix_free(temp6);
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// matrix_free(temp6);
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matrix_free(temp7);
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matrix_free(weights_delta);
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matrix_free(bias_delta);
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@ -358,38 +355,6 @@ double sigmoid(double input) {
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return 1.0 / (1 + exp(-1 * input));
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}
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double sigmoid_derivative(double x) {
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return x * (1.0 - x);
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}
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Matrix* softmax(Matrix* matrix) {
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double total = 0;
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for (int i = 0; i < matrix->rows; i++) {
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for (int j = 0; j < matrix->columns; j++) {
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total += exp(matrix->numbers[i][j]);
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}
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}
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Matrix* result_matrix = matrix_create(matrix->rows, matrix->columns);
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for (int i = 0; i < result_matrix->rows; i++) {
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for (int j = 0; j < result_matrix->columns; j++) {
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result_matrix->numbers[i][j] = exp(matrix->numbers[i][j]) / total;
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}
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}
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return result_matrix;
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}
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double square(double input) {
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return input * input;
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}
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//double loss_function(Matrix* output_matrix, int image_label) {
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// Matrix* temp = matrix_copy(output_matrix);
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//
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// temp->numbers[1][image_label] -= 1;
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// apply(square, temp);
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//
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// matrix_free(temp);
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//
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// return matrix_sum(temp);;
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//}
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}
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@ -1,11 +1,9 @@
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#pragma once
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#include "matrix.h"
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#include "image.h"
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typedef struct {
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int input_size;
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//Matrix* input; as local variable given to function
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// hidden layers
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int hidden_size;
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@ -19,7 +17,6 @@ typedef struct {
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int output_size;
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Matrix* weights_output;
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Matrix* bias_output;
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//Matrix* output; as local variable given to function
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double learning_rate;
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@ -28,16 +25,16 @@ typedef struct {
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static const int MAX_BYTES = 100;
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Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate);
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//void print_network(Neural_Network* network);
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void randomize_network(Neural_Network* network, int scope);
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void free_network(Neural_Network* network);
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void save_network(Neural_Network* network);
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Neural_Network* load_network(char* file);
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void print_network(Neural_Network* network);
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double measure_network_accuracy(Neural_Network* network, Image** images, int amount);
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Matrix* predict_image(Neural_Network* network, Image* image);
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Matrix* predict(Neural_Network* network, Matrix* image_data);
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void train_network(Neural_Network* network, Image *image, int label);
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void batch_train_network(Neural_Network* network, Image** images, int size);
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