augustin64/tipe
1
1
Fork 0
mirror of https://github.com/augustin64/projet-tipe synced 2025-01-23 23:26:25 +01:00
Code Issues Packages Projects Releases Wiki Activity

Add mean squared error (MSE)

Browse Source
This commit is contained in:
Julien Chemillier 2022-10-07 15:32:54 +02:00
parent e4ec06705b
commit 638cada723
2 changed files with 24 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
src/cnn/cnn.c
View File

@ -53,11 +53,11 @@ void forward_propagation(Network* network) {
output_width = network->width[i+1];
activation = k_i->activation;
if (k_i->cnn!=NULL) { // Convolution
if (k_i->cnn) { // Convolution
make_convolution(k_i->cnn, input, output, output_width);
choose_apply_function_matrix(activation, output, output_depth, output_width);
}
else if (k_i->nn!=NULL) { // Full connection
else if (k_i->nn) { // Full connection
if (input_depth==1) { // Vecteur -> Vecteur
make_dense(k_i->nn, input[0][0], output[0][0], input_width, output_width);
} else { // Matrice -> vecteur
@ -80,7 +80,7 @@ void backward_propagation(Network* network, float wanted_number) {
printf_warning("Appel de backward_propagation, incomplet\n");
float* wanted_output = generate_wanted_output(wanted_number);
int n = network->size;
float loss = compute_cross_entropy_loss(network->input[n][0][0], wanted_output, network->width[n]);
float loss = compute_mean_squared_error(network->input[n][0][0], wanted_output, network->width[n]);
int activation, input_depth, input_width, output_depth, output_width;
float*** input;
float*** output;
@ -106,6 +106,18 @@ void backward_propagation(Network* network, float wanted_number) {
free(wanted_output);
}
float compute_mean_squared_error(float* output, float* wanted_output, int len) {
if (len==0) {
printf("Erreur MSE: la longueur de la sortie est de 0 -> division par 0 impossible\n");
return 0.;
}
float loss=0.;
for (int i=0; i < len ; i++) {
loss += (output[i]-wanted_output[i])*(output[i]-wanted_output[i]);
}
return loss/len;
}
float compute_cross_entropy_loss(float* output, float* wanted_output, int len) {
float loss=0.;
for (int i=0; i < len ; i++) {

13
src/cnn/include/cnn.h
View File

@ -7,12 +7,12 @@
/*
* Renvoie si oui ou non (1 ou 0) le neurone va être abandonné
*/
int will_be_drop(int dropout_prob); //CHECKED
int will_be_drop(int dropout_prob);
/*
* Écrit une image 28*28 au centre d'un tableau 32*32 et met à 0 le reste
*/
void write_image_in_network_32(int** image, int height, int width, float** input); //CHECKED
void write_image_in_network_32(int** image, int height, int width, float** input);
/*
* Propage en avant le cnn
@ -22,10 +22,15 @@ void forward_propagation(Network* network);
/*
* Propage en arrière le cnn
*/
void backward_propagation(Network* network, float wanted_number); // TODO
void backward_propagation(Network* network, float wanted_number);
/*
* Renvoie l'erreur du réseau neuronal pour une sortie
* Renvoie l'erreur du réseau neuronal pour une sortie (RMS)
*/
float compute_mean_squared_error(float* output, float* wanted_output, int len);
/*
* Renvoie l'erreur du réseau neuronal pour une sortie (CEL)
*/
float compute_cross_entropy_loss(float* output, float* wanted_output, int len);