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julienChemillier 2023-03-13 18:47:32 +01:00
parent adeee56f83
commit 84e05a732b
2 changed files with 5 additions and 6 deletions
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9
src/cnn/creation.c
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@ -9,7 +9,7 @@
#include "include/creation.h" #include "include/creation.h"
Network* create_network(int max_size, float learning_rate, int dropout, int initialisation, int input_dim, int input_depth) { Network* create_network(int max_size, float learning_rate, int dropout, int activation, int initialisation, int input_dim, int input_depth) {
if (dropout < 0 || dropout > 100) { if (dropout < 0 || dropout > 100) {
printf_error("La probabilité de dropout n'est pas respecté, elle doit être comprise entre 0 et 100\n"); printf_error("La probabilité de dropout n'est pas respecté, elle doit être comprise entre 0 et 100\n");
} }
@ -28,6 +28,7 @@ Network* create_network(int max_size, float learning_rate, int dropout, int init
network->kernel[i] = (Kernel*)nalloc(1, sizeof(Kernel)); network->kernel[i] = (Kernel*)nalloc(1, sizeof(Kernel));
} }
network->kernel[0]->linearisation = DOESNT_LINEARISE; network->kernel[0]->linearisation = DOESNT_LINEARISE;
network->kernel[0]->activation = activation;
network->width[0] = input_dim; network->width[0] = input_dim;
network->depth[0] = input_depth; network->depth[0] = input_depth;
network->kernel[0]->nn = NULL; network->kernel[0]->nn = NULL;
@ -38,8 +39,7 @@ Network* create_network(int max_size, float learning_rate, int dropout, int init
} }
Network* create_network_lenet5(float learning_rate, int dropout, int activation, int initialisation, int input_dim, int input_depth) { Network* create_network_lenet5(float learning_rate, int dropout, int activation, int initialisation, int input_dim, int input_depth) {
Network* network = create_network(8, learning_rate, dropout, initialisation, input_dim, input_depth); Network* network = create_network(8, learning_rate, dropout, activation, initialisation, input_dim, input_depth);
network->kernel[0]->activation = activation;
add_convolution(network, 6, 28, activation); add_convolution(network, 6, 28, activation);
add_average_pooling(network, 14); add_average_pooling(network, 14);
add_convolution(network, 16, 10, activation); add_convolution(network, 16, 10, activation);
@ -51,8 +51,7 @@ Network* create_network_lenet5(float learning_rate, int dropout, int activation,
} }
Network* create_simple_one(float learning_rate, int dropout, int activation, int initialisation, int input_dim, int input_depth) { Network* create_simple_one(float learning_rate, int dropout, int activation, int initialisation, int input_dim, int input_depth) {
Network* network = create_network(3, learning_rate, dropout, initialisation, input_dim, input_depth); Network* network = create_network(3, learning_rate, dropout, activation, initialisation, input_dim, input_depth);
network->kernel[0]->activation = activation;
add_dense_linearisation(network, 80, activation); add_dense_linearisation(network, 80, activation);
add_dense(network, 10, SOFTMAX); add_dense(network, 10, SOFTMAX);
return network; return network;

2
src/cnn/include/creation.h
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@ -7,7 +7,7 @@
/* /*
* Créé un réseau qui peut contenir max_size couche (dont celle d'input et d'output) * Créé un réseau qui peut contenir max_size couche (dont celle d'input et d'output)
*/ */
Network* create_network(int max_size, float learning_rate, int dropout, int initialisation, int input_dim, int input_depth); Network* create_network(int max_size, float learning_rate, int dropout, int activation, int initialisation, int input_dim, int input_depth);
/* /*
* Renvoie un réseau suivant l'architecture LeNet5 * Renvoie un réseau suivant l'architecture LeNet5