rawalcher/c-net
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relu placeholder, softmax und predict

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Tocuro 2023-09-21 08:56:47 +02:00
parent 614df3c4a1
commit 3eaf2ae810
2 changed files with 64 additions and 33 deletions
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90
neuronal_network.c
View file

@ -2,6 +2,7 @@
#include "neuronal_network.h"
#include <stdio.h>
#include <time.h>
#include <math.h>
Neural_Network* new_network(int input_size, int hidden_size, int output_size, double learning_rate){
Neural_Network *network = malloc(sizeof(Neural_Network));
@ -104,38 +105,65 @@ Neural_Network* load_network(char* file) {
return saved_network;
}
//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]->label) {
// num_correct++;
// }
// matrix_free(prediction);
// }
// return 1.0 * num_correct / 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]->label) {
num_correct++;
}
matrix_free(prediction);
}
return 1.0 * num_correct / amount;
}
//Matrix* predict_image(Neural_Network* network, Image*);
Matrix* predict_image(Neural_Network* network, Image* image){
Matrix* image_data = matrix_flatten(image->pixel_values, 0);
Matrix* res = predict(network, image_data);
matrix_free(image_data);
return res;
}
//Matrix* predict(Neural_Network* network, Matrix* image_data) {
// Matrix* hidden1_outputs = apply(relu, 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* hidden3_outputs = apply(relu, add(dot(network->weights_3, hidden2_outputs), network->bias_3));
//
// Matrix* final_outputs = apply(relu, dot(network->weights_output, hidden3_outputs));
//
// Matrix* result = softmax(final_outputs);
//
// matrix_free(hidden1_outputs);
// matrix_free(hidden2_outputs);
// matrix_free(hidden3_outputs);
// matrix_free(final_outputs);
//
// return result;
//}
Matrix* predict(Neural_Network* network, Matrix* image_data) {
Matrix* hidden1_outputs = apply(relu, 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* hidden3_outputs = apply(relu, add(dot(network->weights_3, hidden2_outputs), network->bias_3));
Matrix* final_outputs = apply(relu, dot(network->weights_output, hidden3_outputs));
Matrix* result = softmax(final_outputs);
matrix_free(hidden1_outputs);
matrix_free(hidden2_outputs);
matrix_free(hidden3_outputs);
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);
//void batch_train_network(Neural_Network* network, Image** images, int size);
double relu(double input) {
return 1.0;
//TODO: relu formel
}
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;
}

7
neuronal_network.h
View file

@ -35,8 +35,11 @@ void save_network(Neural_Network* network);
Neural_Network* load_network(char* file);
double predict_images(Neural_Network* network, Image** images, int amount);
Matrix* predict_image(Neural_Network* network, Image*);
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 batch_train_network(Neural_Network* network, Image** images, int size);
void batch_train_network(Neural_Network* network, Image** images, int size);
double relu(double input);
Matrix* softmax(Matrix* matrix);