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Update backpropagation (It works now)

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augustin64 2023-01-25 11:18:47 +01:00
parent f943228e80
commit 220d0a71be
3 changed files with 23 additions and 1 deletions
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2
src/cnn/backpropagation.c
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@ -86,7 +86,7 @@ void backward_linearisation(Kernel_nn* ker, float*** input, float*** input_z, fl
for (int k=0; k < dim_input; k++) {
for (int l=0; l < dim_input; l++) {
for (int j=0; j < size_output; j++) {
ker->d_weights[cpt][j] += input[i][k][l]*output[j];
ker->d_weights[cpt][j] += input[i][k][l]*output[j]/(depth_input*dim_input*dim_input*size_output);
}
cpt++;
}

2
src/cnn/include/update.h
View File

@ -3,6 +3,8 @@
#ifndef DEF_UPDATE_H
#define DEF_UPDATE_H
#define MAX_RESEAU 100000000
/*
* Met à jours les poids à partir de données obtenus après plusieurs backpropagations
* Puis met à 0 tous les d_weights

20
src/cnn/update.c
View File

@ -26,6 +26,11 @@ void update_weights(Network* network, Network* d_network) {
for (int d=0; d<k_size; d++) {
cnn->w[a][b][c][d] -= network->learning_rate * d_cnn->d_w[a][b][c][d];
d_cnn->d_w[a][b][c][d] = 0;
if (cnn->w[a][b][c][d] > MAX_RESEAU)
cnn->w[a][b][c][d] = MAX_RESEAU;
else if (cnn->w[a][b][c][d] < -MAX_RESEAU)
cnn->w[a][b][c][d] = -MAX_RESEAU;
}
}
}
@ -48,6 +53,11 @@ void update_weights(Network* network, Network* d_network) {
for (int b=0; b<output_width; b++) {
nn->weights[a][b] -= network->learning_rate * d_nn->d_weights[a][b];
d_nn->d_weights[a][b] = 0;
if (nn->weights[a][b] > MAX_RESEAU)
nn->weights[a][b] = MAX_RESEAU;
else if (nn->weights[a][b] < -MAX_RESEAU)
nn->weights[a][b] = -MAX_RESEAU;
}
}
}
@ -77,6 +87,11 @@ void update_bias(Network* network, Network* d_network) {
for (int c=0; c<output_width; c++) {
cnn->bias[a][b][c] -= network->learning_rate * d_cnn->d_bias[a][b][c];
d_cnn->d_bias[a][b][c] = 0;
if (cnn->bias[a][b][c] > MAX_RESEAU)
cnn->bias[a][b][c] = MAX_RESEAU;
else if (cnn->bias[a][b][c] < -MAX_RESEAU)
cnn->bias[a][b][c] = -MAX_RESEAU;
}
}
}
@ -86,6 +101,11 @@ void update_bias(Network* network, Network* d_network) {
for (int a=0; a<output_width; a++) {
nn->bias[a] -= network->learning_rate * d_nn->d_bias[a];
d_nn->d_bias[a] = 0;
if (nn->bias[a] > MAX_RESEAU)
nn->bias[a] = MAX_RESEAU;
else if (nn->bias[a] < -MAX_RESEAU)
nn->bias[a] = -MAX_RESEAU;
}
} else { // Pooling
(void)0; // Ne rien faire pour la couche pooling