Split creation.c & create models.c

src/cnn/creation.c

@@ -34,73 +34,6 @@ Network* create_network(int max_size, float learning_rate, int dropout, int init
     return network;
 }
 
-Network* create_network_lenet5(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth) {
-    Network* network = create_network(8, learning_rate, dropout, initialisation, input_width, input_depth);
-    add_convolution(network, 5, 6, 1, 0, activation);
-    add_average_pooling(network, 2, 2, 0);
-    add_convolution(network, 5, 16, 1, 0, activation);
-    add_average_pooling(network, 2, 2, 0);
-    add_dense_linearisation(network, 120, activation);
-    add_dense(network, 84, activation);
-    add_dense(network, 10, SOFTMAX);
-    return network;
-}
-
-Network* create_network_alexnet(float learning_rate, int dropout, int activation, int initialisation, int size_output) {
-    Network* network = create_network(12, learning_rate, dropout, initialisation, 227, 3);
-    add_convolution(network, 11, 96, 4, 0, activation);
-    add_average_pooling(network, 3, 2, 0);
-    add_convolution(network, 5, 256, 1, 2, activation);
-    add_average_pooling(network, 3, 2, 0);
-    add_convolution(network, 3, 384, 1, 1, activation);
-    add_convolution(network, 3, 384, 1, 1, activation);
-    add_convolution(network, 3, 256, 1, 1, activation);
-    add_average_pooling(network, 3, 2, 0);
-    add_dense_linearisation(network, 4096, activation);
-    add_dense(network, 4096, activation);
-    add_dense(network, size_output, SOFTMAX);
-    return network;
-}
-
-Network* create_network_VGG16(float learning_rate, int dropout, int activation, int initialisation, int size_output) {
-    Network* network = create_network(23, learning_rate, dropout, initialisation, 256, 3);
-    add_convolution(network, 3, 64, 1, 0, activation); // Conv3-64
-    add_convolution(network, 3, 64, 1, 0, activation); // Conv3-64
-    add_average_pooling(network, 2, 2, 0); // Max Pool
-
-    add_convolution(network, 3, 128, 1, 0, activation); // Conv3-128
-    add_convolution(network, 1, 128, 1, 0, activation); // Conv1-128
-    add_average_pooling(network, 2, 2, 0); // Max Pool
-
-    add_convolution(network, 3, 256, 1, 0, activation); // Conv3-256
-    add_convolution(network, 3, 256, 1, 0, activation); // Conv3-256
-    add_convolution(network, 1, 256, 1, 0, activation); // Conv1-256
-    add_average_pooling(network, 2, 2, 0); // Max Pool
-
-    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
-    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
-    add_convolution(network, 1, 512, 1, 0, activation); // Conv1-512
-    add_average_pooling(network, 2, 2, 0); // Max Pool
-
-    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
-    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
-    add_convolution(network, 1, 512, 1, 0, activation); // Conv1-512
-    add_average_pooling(network, 2, 2, 0); // Max Pool
-
-    add_dense_linearisation(network, 2048, activation);
-    add_dense(network, 2048, activation);
-    add_dense(network, 256, activation);
-    add_dense(network, size_output, SOFTMAX);
-    return network;
-}
-
-Network* create_simple_one(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth) {
-    Network* network = create_network(3, learning_rate, dropout, initialisation, input_width, input_depth);
-    add_dense_linearisation(network, 80, activation);
-    add_dense(network, 10, SOFTMAX);
-    return network;
-}
-
 void create_a_cube_input_layer(Network* network, int pos, int depth, int dim) {
     network->input[pos] = (float***)nalloc(depth, sizeof(float**));
     for (int i=0; i < depth; i++) {

src/cnn/include/creation.h

@@ -9,28 +9,6 @@
 */
 Network* create_network(int max_size, float learning_rate, int dropout, int initialisation, int input_width, int input_depth);
 
-/*
-* Renvoie un réseau suivant l'architecture LeNet5
-*/
-Network* create_network_lenet5(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth);
-
-/*
-* Renvoie un réseau suivant l'architecture AlexNet
-* C'est à dire en entrée 3x227x227 et une sortie de taille 'size_output'
-*/
-Network* create_network_alexnet(float learning_rate, int dropout, int activation, int initialisation, int size_output);
-
-/*
-* Renvoie un réseau suivant l'architecture VGG16 modifiée pour prendre en entrée 3x256x256
-* et une sortie de taille 'size_output'
-*/
-Network* create_network_VGG16(float learning_rate, int dropout, int activation, int initialisation, int size_output);
-
-/*
-* Renvoie un réseau sans convolution, similaire à celui utilisé dans src/dense
-*/
-Network* create_simple_one(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth);
-
 /*
 * Créé et alloue de la mémoire à une couche de type input cube
 */

src/cnn/include/models.h

@@ -0,0 +1,29 @@
+#include <stdlib.h>
+#include <stdio.h>
+
+#include "struct.h"
+
+#ifndef DEF_MODELS_H
+#define DEF_MODELS_H
+/*
+* Renvoie un réseau suivant l'architecture LeNet5
+*/
+Network* create_network_lenet5(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth);
+
+/*
+* Renvoie un réseau suivant l'architecture AlexNet
+* C'est à dire en entrée 3x227x227 et une sortie de taille 'size_output'
+*/
+Network* create_network_alexnet(float learning_rate, int dropout, int activation, int initialisation, int size_output);
+
+/*
+* Renvoie un réseau suivant l'architecture VGG16 modifiée pour prendre en entrée 3x256x256
+* et une sortie de taille 'size_output'
+*/
+Network* create_network_VGG16(float learning_rate, int dropout, int activation, int initialisation, int size_output);
+
+/*
+* Renvoie un réseau sans convolution, similaire à celui utilisé dans src/dense
+*/
+Network* create_simple_one(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth);
+#endif

src/cnn/models.c

@@ -0,0 +1,75 @@
+#include <stdlib.h>
+#include <stdio.h>
+
+#include "include/creation.h"
+#include "include/function.h"
+#include "include/struct.h"
+
+#include "include/models.h"
+
+Network* create_network_lenet5(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth) {
+    Network* network = create_network(8, learning_rate, dropout, initialisation, input_width, input_depth);
+    add_convolution(network, 5, 6, 1, 0, activation);
+    add_average_pooling(network, 2, 2, 0);
+    add_convolution(network, 5, 16, 1, 0, activation);
+    add_average_pooling(network, 2, 2, 0);
+    add_dense_linearisation(network, 120, activation);
+    add_dense(network, 84, activation);
+    add_dense(network, 10, SOFTMAX);
+    return network;
+}
+
+Network* create_network_alexnet(float learning_rate, int dropout, int activation, int initialisation, int size_output) {
+    Network* network = create_network(12, learning_rate, dropout, initialisation, 227, 3);
+    add_convolution(network, 11, 96, 4, 0, activation);
+    add_average_pooling(network, 3, 2, 0);
+    add_convolution(network, 5, 256, 1, 2, activation);
+    add_average_pooling(network, 3, 2, 0);
+    add_convolution(network, 3, 384, 1, 1, activation);
+    add_convolution(network, 3, 384, 1, 1, activation);
+    add_convolution(network, 3, 256, 1, 1, activation);
+    add_average_pooling(network, 3, 2, 0);
+    add_dense_linearisation(network, 4096, activation);
+    add_dense(network, 4096, activation);
+    add_dense(network, size_output, SOFTMAX);
+    return network;
+}
+
+Network* create_network_VGG16(float learning_rate, int dropout, int activation, int initialisation, int size_output) {
+    Network* network = create_network(23, learning_rate, dropout, initialisation, 256, 3);
+    add_convolution(network, 3, 64, 1, 0, activation); // Conv3-64
+    add_convolution(network, 3, 64, 1, 0, activation); // Conv3-64
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_convolution(network, 3, 128, 1, 0, activation); // Conv3-128
+    add_convolution(network, 1, 128, 1, 0, activation); // Conv1-128
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_convolution(network, 3, 256, 1, 0, activation); // Conv3-256
+    add_convolution(network, 3, 256, 1, 0, activation); // Conv3-256
+    add_convolution(network, 1, 256, 1, 0, activation); // Conv1-256
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
+    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
+    add_convolution(network, 1, 512, 1, 0, activation); // Conv1-512
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
+    add_convolution(network, 3, 512, 1, 0, activation); // Conv3-512
+    add_convolution(network, 1, 512, 1, 0, activation); // Conv1-512
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_dense_linearisation(network, 2048, activation);
+    add_dense(network, 2048, activation);
+    add_dense(network, 256, activation);
+    add_dense(network, size_output, SOFTMAX);
+    return network;
+}
+
+Network* create_simple_one(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth) {
+    Network* network = create_network(3, learning_rate, dropout, initialisation, input_width, input_depth);
+    add_dense_linearisation(network, 80, activation);
+    add_dense(network, 10, SOFTMAX);
+    return network;
+}