rawalcher/c-net
Archived
1
0
Fork 0
Code Issues 5 Pull requests Projects Releases 2 Packages Wiki Activity Actions

Merge remote-tracking branch 'origin/Development' into Development

Browse source
This commit is contained in:
Jakob Stornig 2023-09-21 11:22:43 +02:00
commit 0f7a0a5f11
2 changed files with 7 additions and 3 deletions
Download patch file Download diff file Expand all files Collapse all files

8
neuronal_network.c
View file

@ -19,7 +19,7 @@ Neural_Network* new_network(int input_size, int hidden_size, int output_size, do
network->bias_1 = matrix_create(hidden_size, 1);
network->bias_2 = 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);
@ -34,6 +34,7 @@ void randomize_network(Neural_Network* network, int scope){
matrix_randomize(network->bias_1, scope);
matrix_randomize(network->bias_2, scope);
matrix_randomize(network->bias_3, scope);
matrix_randomize(network->bias_output, scope);
}
//void print_network(Neural_Network* network){};
@ -46,6 +47,7 @@ void free_network(Neural_Network* network){
matrix_free(network->bias_1);
matrix_free(network->bias_2);
matrix_free(network->bias_3);
matrix_free(network->bias_output);
free(network);
}
@ -84,6 +86,7 @@ void save_network(Neural_Network* network) {
matrix_save(network->weights_3, file_name);
// save output weights
matrix_save(network->bias_output, file_name);
matrix_save(network->weights_output, file_name);
printf("Network Saved!");
@ -119,6 +122,7 @@ Neural_Network* load_network(char* file) {
saved_network->weights_2 = load_next_matrix(save_file);
saved_network->bias_3 = load_next_matrix(save_file);
saved_network->weights_3 = load_next_matrix(save_file);
saved_network->bias_output = load_next_matrix(save_file);
saved_network->weights_output = load_next_matrix(save_file);
// return saved network
@ -152,7 +156,7 @@ Matrix* predict(Neural_Network* network, Matrix* image_data) {
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* final_outputs = apply(relu, add(dot(network->weights_output, hidden3_outputs), network->bias_output));
Matrix* result = softmax(final_outputs);