rawalcher/c-net
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fixed to compile

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This commit is contained in:
Jakob Stornig 2023-09-19 20:32:19 +02:00
parent 963eef1d33
commit 30317791e4
2 changed files with 45 additions and 44 deletions
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2
CMakeLists.txt
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@ -3,5 +3,5 @@ project(c_net C)
set(CMAKE_C_STANDARD 11) set(CMAKE_C_STANDARD 11)
add_executable(c_net main.c matrix.c image.c) add_executable(c_net main.c matrix.c image.c neuronal_network.c)
target_link_libraries(c_net m) target_link_libraries(c_net m)

87
neuronal_network.c
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@ -4,20 +4,20 @@
#include <time.h> #include <time.h>
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
network.input_size = input_size; network->hidden_size = hidden_size;
network.hidden_size = hidden_size; network->input_size = input_size;
network.output_size = output_size; network->output_size = output_size;
network.learning_rate = learning_rate; network->learning_rate = learning_rate;
network.weights_1 = matrix_randomize(matrix_create(hidden_size, input_size)); network->weights_1 = matrix_create(hidden_size, input_size);
network.weights_2 = matrix_randomize(matrix_create(hidden_size, hidden_size)); network->weights_2 = matrix_create(hidden_size, hidden_size);
network.weights_3 = matrix_randomize(matrix_create(hidden_size, hidden_size)); network->weights_3 = matrix_create(hidden_size, hidden_size);
network.weights_output = matrix_randomize(matrix_create(output_size, hidden_size)); network->weights_output = matrix_create(output_size, hidden_size);
network.bias_1 = matrix_randomize(matrix_create(hidden_size, 1)); network->bias_1 = matrix_create(hidden_size, 1);
network.bias_2 = matrix_randomize(matrix_create(hidden_size, 1)); network->bias_2 = matrix_create(hidden_size, 1);
network.bias_3 = matrix_randomize(matrix_create(hidden_size, 1)); network->bias_3 = matrix_create(hidden_size, 1);
//network.bias_output = matrix_create(output_size, 1); // do we need it? //network.bias_output = matrix_create(output_size, 1); // do we need it?
return network; return network;
@ -60,7 +60,7 @@ void save_network(Neural_Network* network) {
fprintf(save_file, "%d\n", network->output_size); fprintf(save_file, "%d\n", network->output_size);
// close the file // close the file
fclose(file_name); fclose(save_file);
// save first layer // save first layer
matrix_save(network->bias_1, file_name); matrix_save(network->bias_1, file_name);
@ -81,40 +81,41 @@ void save_network(Neural_Network* network) {
} }
Neural_Network* load_network(char* file) { Neural_Network* load_network(char* file) {
return NULL;
} }
double predict_images(Neural_Network* network, Image** images, int amount) { //double predict_images(Neural_Network* network, Image** images, int amount) {
int num_correct = 0; // int num_correct = 0;
for (int i = 0; i < amount; i++) { // for (int i = 0; i < amount; i++) {
Matrix* prediction = predict_image(network, images[i]); // Matrix* prediction = predict_image(network, images[i]);
if (matrix_argmax(prediction) == images[i]->image_label) { // if (matrix_argmax(prediction) == images[i]->label) {
num_correct++; // num_correct++;
} // }
matrix_free(prediction); // matrix_free(prediction);
} // }
return 1.0 * num_correct / amount; // return 1.0 * num_correct / amount;
} //}
Matrix* predict_image(Neural_Network* network, Image*);
Matrix* predict(Neural_Network* network, Matrix* image_data) { //Matrix* predict_image(Neural_Network* network, Image*);
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* predict(Neural_Network* network, Matrix* image_data) {
// Matrix* hidden1_outputs = apply(relu, add(dot(network->weights_1, image_data), network->bias_1));
Matrix* hidden3_outputs = apply(relu, add(dot(network->weights_3, hidden2_outputs), network->bias_3)); //
// Matrix* hidden2_outputs = apply(relu, add(dot(network->weights_2, hidden1_outputs), network->bias_2));
Matrix* final_outputs = apply(relu, dot(network->weights_output, hidden3_outputs)); //
// Matrix* hidden3_outputs = apply(relu, add(dot(network->weights_3, hidden2_outputs), network->bias_3));
Matrix* result = softmax(final_outputs); //
// Matrix* final_outputs = apply(relu, dot(network->weights_output, hidden3_outputs));
matrix_free(hidden1_outputs); //
matrix_free(hidden2_outputs); // Matrix* result = softmax(final_outputs);
matrix_free(hidden3_outputs); //
matrix_free(final_outputs); // matrix_free(hidden1_outputs);
// matrix_free(hidden2_outputs);
return result; // matrix_free(hidden3_outputs);
} // matrix_free(final_outputs);
//
// return result;
//}
void train_network(Neural_Network* network, Matrix* input, Matrix* output); 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);