Creation of the folder 'include'

src/cnn/creation.c

@@ -1,8 +1,8 @@
 #include <stdio.h>
 #include <stdlib.h>
-#include "creation.h"
+#include "include/creation.h"
-#include "function.h"
+#include "include/function.h"
-#include "initialisation.h"
+#include "include/initialisation.h"
 
 Network* create_network(int max_size, int dropout, int initialisation, int input_dim, int input_depth) {
     if (dropout < 0 || dropout > 100) {
@@ -86,28 +86,27 @@ void add_average_pooling_flatten(Network* network, int kernel_size, int activati
     network->size++;
 }
 
-void add_convolution(Network* network, int nb_filter, int kernel_size, int activation) {
+void add_convolution(Network* network, int depth_output, int kernel_size, int activation) {
     int n = network->size;
     if (network->max_size == n) {
-        printf("Impossible de rajouter une couche de convolution, le réseau est déjà plein\n");
+        printf("Impossible de rajouter une couche de convolution, le réseau est déjà plein \n");
         return;
     }
-    int r = network->depth[n-1];
+    int depth_input = network->depth[n-1];
-    int c = nb_filter;
     network->kernel[n]->nn = NULL;
     network->kernel[n]->activation = activation;
     network->kernel[n]->cnn = (Kernel_cnn*)malloc(sizeof(Kernel_cnn));
     Kernel_cnn* cnn = network->kernel[n]->cnn;
 
     cnn->k_size = kernel_size;
-    cnn->rows = r;
+    cnn->rows = depth_input;
-    cnn->columns = c;
+    cnn->columns = depth_output;
-    cnn->w = (float****)malloc(sizeof(float***)*r);
+    cnn->w = (float****)malloc(sizeof(float***)*depth_input);
-    cnn->d_w = (float****)malloc(sizeof(float***)*r);
+    cnn->d_w = (float****)malloc(sizeof(float***)*depth_input);
-    for (int i=0; i < r; i++) {
+    for (int i=0; i < depth_input; i++) {
-        cnn->w[i] = (float***)malloc(sizeof(float**)*c);
+        cnn->w[i] = (float***)malloc(sizeof(float**)*depth_output);
-        cnn->d_w[i] = (float***)malloc(sizeof(float**)*c);
+        cnn->d_w[i] = (float***)malloc(sizeof(float**)*depth_output);
-        for (int j=0; j < c; j++) {
+        for (int j=0; j < depth_output; j++) {
             cnn->w[i][j] = (float**)malloc(sizeof(float*)*kernel_size);
             cnn->d_w[i][j] = (float**)malloc(sizeof(float*)*kernel_size);
             for (int k=0; k < kernel_size; k++) {
@@ -116,9 +115,9 @@ void add_convolution(Network* network, int nb_filter, int kernel_size, int activ
             }
         }
     }
-    cnn->bias = (float***)malloc(sizeof(float**)*c);
+    cnn->bias = (float***)malloc(sizeof(float**)*depth_output);
-    cnn->d_bias = (float***)malloc(sizeof(float**)*c);
+    cnn->d_bias = (float***)malloc(sizeof(float**)*depth_output);
-    for (int i=0; i < c; i++) {
+    for (int i=0; i < depth_output; i++) {
         cnn->bias[i] = (float**)malloc(sizeof(float*)*kernel_size);
         cnn->d_bias[i] = (float**)malloc(sizeof(float*)*kernel_size);
         for (int j=0; j < kernel_size; j++) {
@@ -126,13 +125,13 @@ void add_convolution(Network* network, int nb_filter, int kernel_size, int activ
             cnn->d_bias[i][j] = (float*)malloc(sizeof(float)*kernel_size);
         }
     }
-    create_a_cube_input_layer(network, n, c, network->width[n-1] - 2*(kernel_size/2));
+    create_a_cube_input_layer(network, n, depth_output, network->width[n-1] - 2*(kernel_size/2));
     int n_int = network->width[n-1]*network->width[n-1]*network->depth[n-1];
     int n_out = network->width[n]*network->width[n]*network->depth[n];
-    initialisation_3d_matrix(network->initialisation, cnn->bias, c, kernel_size, kernel_size, n_int+n_out);
+    initialisation_3d_matrix(network->initialisation, cnn->bias, depth_output, kernel_size, kernel_size, n_int+n_out);
-    initialisation_3d_matrix(ZERO, cnn->d_bias, c, kernel_size, kernel_size, n_int+n_out);
+    initialisation_3d_matrix(ZERO, cnn->d_bias, depth_output, kernel_size, kernel_size, n_int+n_out);
-    initialisation_4d_matrix(network->initialisation, cnn->w, r, c, kernel_size, kernel_size, n_int+n_out);
+    initialisation_4d_matrix(network->initialisation, cnn->w, depth_input, depth_output, kernel_size, kernel_size, n_int+n_out);
-    initialisation_4d_matrix(ZERO, cnn->d_w, r, c, kernel_size, kernel_size, n_int+n_out);
+    initialisation_4d_matrix(ZERO, cnn->d_w, depth_input, depth_output, kernel_size, kernel_size, n_int+n_out);
     network->size++;
 }
 

src/cnn/free.c

@@ -1,6 +1,6 @@
 #include <stdlib.h>
 #include <stdio.h>
-#include "free.h"
+#include "include/free.h"
 
 void free_a_cube_input_layer(Network* network, int pos, int depth, int dim) {
     for (int i=0; i < depth; i++) {

src/cnn/function.c

@@ -1,7 +1,7 @@
 #include <stdio.h>
 #include <math.h>
 #include <float.h>
-#include "function.h"
+#include "include/function.h"
 
 float max(float a, float b) {
     return a < b ? b:a;

src/cnn/include/creation.h

@@ -16,7 +16,7 @@ Network* create_network_lenet5(int dropout, int activation, int initialisation);
 /*
 * Créé et alloue de la mémoire à une couche de type input cube
 */
-void create_a_cube_input_layer(Network* network, int pos, int depth, int dim);
+void create_a_cube_input_layer(Network* network, int pos, int depth, int dim); // CHECKED
 
 /*
 * Créé et alloue de la mémoire à une couche de type ligne

src/cnn/include/main.h

@@ -7,12 +7,12 @@
 /*
 * Renvoie si oui ou non (1 ou 0) le neurone va être abandonné
 */
-int will_be_drop(int dropout_prob);
+int will_be_drop(int dropout_prob); //CHECKED
 
 /*
 * 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);
+void write_image_in_network_32(int** image, int height, int width, float** input); //CHECKED
 
 /*
 * Propage en avant le cnn

src/cnn/initialisation.c

@@ -1,6 +1,6 @@
 #include <stdlib.h>
 #include <math.h>
-#include "initialisation.h"
+#include "include/initialisation.h"
 
 
 void initialisation_1d_matrix(int initialisation, float* matrix, int rows, int n) { //NOT FINISHED

src/cnn/main.c

@@ -7,7 +7,7 @@
 #include "creation.c"
 #include "make.c"
 
-#include "main.h"
+#include "include/main.h"
 
 // Augmente les dimensions de l'image d'entrée
 #define PADDING_INPUT 2
@@ -18,8 +18,8 @@ int will_be_drop(int dropout_prob) {
 
 void write_image_in_network_32(int** image, int height, int width, float** input) {
     for (int i=0; i < height+2*PADDING_INPUT; i++) {
-        for (int j=PADDING_INPUT; j < width+2*PADDING_INPUT; j++) {
+        for (int j=0; j < width+2*PADDING_INPUT; j++) {
-            if (i < PADDING_INPUT || i > height+PADDING_INPUT || j < PADDING_INPUT || j > width+PADDING_INPUT) {
+            if (i < PADDING_INPUT || i >= height+PADDING_INPUT || j < PADDING_INPUT || j >= width+PADDING_INPUT) {
                 input[i][j] = 0.;
             }
             else {

src/cnn/make.c

@@ -1,5 +1,5 @@
 #include <stdio.h>
-#include "make.h"
+#include "include/make.h"
 
 void make_convolution(float*** input, Kernel_cnn* kernel, float*** output, int output_dim) {
     //NOT FINISHED, MISS CONDITIONS ON THE CONVOLUTION