From 5a34311dfbb7d7a9d0307238fb99efb7f38cd219 Mon Sep 17 00:00:00 2001
From: augustin64 <augustin.lucas64@gmail.com>
Date: Tue, 8 Nov 2022 19:57:13 +0100
Subject: [PATCH] Update cnn utils

---
 src/cnn/utils.c | 23 ++++++++++++++---------
 1 file changed, 14 insertions(+), 9 deletions(-)

diff --git a/src/cnn/utils.c b/src/cnn/utils.c
index d794f6a..d02f73b 100644
--- a/src/cnn/utils.c
+++ b/src/cnn/utils.c
@@ -12,12 +12,13 @@
 if (network1->var != network2->var) {                                               \
     printf_error("network1->" name " et network2->" name " ne sont pas égaux\n");   \
     if (indice != -1) {                                                             \
-        printf(BOLDBLUE"[ INFO_ ]"RESET" indice: %d\n", indice);                    \
+        printf(BOLDBLUE"[ INFO_ ]" RESET " indice: %d\n", indice);                    \
         }                                                                           \
     return false;                                                                   \
 }
 
 bool equals_networks(Network* network1, Network* network2) {
+    int output_dim;
     checkEquals(size, "size", -1);
     checkEquals(initialisation, "initialisation", -1);
     checkEquals(dropout, "dropout", -1);
@@ -50,12 +51,13 @@ bool equals_networks(Network* network1, Network* network2) {
             }
         } else {
             // Type CNN
+            output_dim = network1->width[i];
             checkEquals(kernel[i]->cnn->k_size, "kernel[i]->k_size", i);
             checkEquals(kernel[i]->cnn->rows, "kernel[i]->rows", i);
             checkEquals(kernel[i]->cnn->columns, "kernel[i]->columns", i);
             for (int j=0; j < network1->kernel[i]->cnn->columns; j++) {
-                for (int k=0; k < network1->kernel[i]->cnn->k_size; k++) {
-                    for (int l=0; l < network1->kernel[i]->cnn->k_size; l++) {
+                for (int k=0; k < output_dim; k++) {
+                    for (int l=0; l < output_dim; l++) {
                         checkEquals(kernel[i]->cnn->bias[j][k][l], "kernel[i]->cnn->bias[j][k][l]", l);
                     }
                 }
@@ -87,6 +89,7 @@ Network* copy_network(Network* network) {
     int rows;
     int k_size;
     int columns;
+    int output_dim;
 
     copyVar(dropout);
     copyVar(learning_rate);
@@ -149,6 +152,8 @@ Network* copy_network(Network* network) {
             rows = network->kernel[i]->cnn->rows;
             k_size = network->kernel[i]->cnn->k_size;
             columns = network->kernel[i]->cnn->columns;
+            output_dim = network->width[i];
+            
 
             network_cp->kernel[i]->nn = NULL;
             network_cp->kernel[i]->cnn = (Kernel_cnn*)malloc(sizeof(Kernel_cnn));
@@ -160,12 +165,12 @@ Network* copy_network(Network* network) {
             network_cp->kernel[i]->cnn->bias = (float***)malloc(sizeof(float**)*columns);
             network_cp->kernel[i]->cnn->d_bias = (float***)malloc(sizeof(float**)*columns);
             for (int j=0; j < columns; j++) {
-                network_cp->kernel[i]->cnn->bias[j] = (float**)malloc(sizeof(float*)*k_size);
-                network_cp->kernel[i]->cnn->d_bias[j] = (float**)malloc(sizeof(float*)*k_size);
-                for (int k=0; k < k_size; k++) {
-                    network_cp->kernel[i]->cnn->bias[j][k] = (float*)malloc(sizeof(float)*k_size);
-                    network_cp->kernel[i]->cnn->d_bias[j][k] = (float*)malloc(sizeof(float)*k_size);
-                    for (int l=0; l < k_size; l++) {
+                network_cp->kernel[i]->cnn->bias[j] = (float**)malloc(sizeof(float*)*output_dim);
+                network_cp->kernel[i]->cnn->d_bias[j] = (float**)malloc(sizeof(float*)*output_dim);
+                for (int k=0; k < output_dim; k++) {
+                    network_cp->kernel[i]->cnn->bias[j][k] = (float*)malloc(sizeof(float)*output_dim);
+                    network_cp->kernel[i]->cnn->d_bias[j][k] = (float*)malloc(sizeof(float)*output_dim);
+                    for (int l=0; l < output_dim; l++) {
                         copyVar(kernel[i]->cnn->bias[j][k][l]);
                         network_cp->kernel[i]->cnn->d_bias[j][k][l] = 0.;
                     }