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Update multithreading

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augustin64 2022-11-23 10:41:19 +01:00
parent 18900f3fe9
commit 11ce67f845
6 changed files with 31 additions and 6 deletions
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4
src/cnn/creation.c
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@ -28,8 +28,8 @@ Network* create_network(int max_size, float learning_rate, int dropout, int init
network->depth[0] = input_depth;
network->kernel[0]->nn = NULL;
network->kernel[0]->cnn = NULL;
create_a_cube_input_layer(network, 0, input_depth, input_dim);
network->input_z[0] = NULL; // As we don't backpropagate the input
create_a_cube_input_layer(network, 0, input_depth, input_dim);
create_a_cube_input_z_layer(network, 0, input_depth, input_dim);
return network;
}

8
src/cnn/train.c
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@ -193,13 +193,15 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
}
train_parameters[k]->network = copy_network(network);
train_parameters[k]->start = BATCHES*j + (nb_images_total/BATCHES)*k;
pthread_create( &tid[j], NULL, train_thread, (void*) train_parameters[k]);
pthread_create( &tid[k], NULL, train_thread, (void*) train_parameters[k]);
}
for (int k=0; k < nb_threads; k++) {
// On attend la terminaison de chaque thread un à un
pthread_join( tid[j], NULL );
pthread_join( tid[k], NULL );
accuracy += train_parameters[k]->accuracy / (float) nb_images_total;
// TODO patch_network(network, train_parameters[k]->network, train_parameters[k]->nb_images);
update_weights(network, train_parameters[k]->network);
update_bias(network, train_parameters[k]->network);
free_network(train_parameters[k]->network);
}
current_accuracy = accuracy * nb_images_total/((j+1)*BATCHES);

14
src/cnn/utils.c
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@ -212,5 +212,19 @@ Network* copy_network(Network* network) {
}
}
network_cp->input_z = (float****)malloc(sizeof(float***)*size);
for (int i=0; i < size; i++) { // input_z[size][couche->depth][couche->dim][couche->dim]
network_cp->input_z[i] = (float***)malloc(sizeof(float**)*network->depth[i]);
for (int j=0; j < network->depth[i]; j++) {
network_cp->input_z[i][j] = (float**)malloc(sizeof(float*)*network->width[i]);
for (int k=0; k < network->width[i]; k++) {
network_cp->input_z[i][j][k] = (float*)malloc(sizeof(float)*network->width[i]);
for (int l=0; l < network->width[i]; l++) {
network_cp->input_z[i][j][k][l] = 0.;
}
}
}
}
return network_cp;
}

2
src/mnist/main.c
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@ -210,7 +210,7 @@ void train(int epochs, int layers, int neurons, char* recovery, char* image_file
pthread_create( &tid[j], NULL, train_thread, (void*) train_parameters[j]);
}
for(int j=0; j < nb_threads; j++ ) {
// On join les threads créés sur le CPU
// On récupère les threads créés sur le CPU
pthread_join( tid[j], NULL );
accuracy += train_parameters[j]->accuracy / (float) nb_images_total;

6
test/cnn_neuron_io.c
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@ -7,6 +7,7 @@
#include "../src/cnn/include/neuron_io.h"
#include "../src/cnn/include/creation.h"
#include "../src/cnn/include/utils.h"
#include "../src/cnn/include/free.h"
#include "../src/include/colors.h"
@ -30,5 +31,10 @@ int main() {
}
printf(GREEN "OK\n" RESET);
printf("Libération de la mémoire\n");
free_network(network);
free_network(network2);
printf(GREEN "OK\n" RESET);
return 0;
}

3
test/cnn_utils.c
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@ -4,6 +4,7 @@
#include "../src/include/colors.h"
#include "../src/cnn/include/creation.h"
#include "../src/cnn/include/utils.h"
#include "../src/cnn/include/free.h"
#include "../src/include/colors.h"
int main() {
@ -22,5 +23,7 @@ int main() {
}
printf(GREEN "OK\n" RESET);
free_network(network_cp);
free_network(network);
return 0;
}