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Removal of useless lines

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This commit is contained in:
julienChemillier 2023-05-14 18:22:29 +02:00
parent 772b3511cf
commit 8de03863fa
2 changed files with 5 additions and 9 deletions
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12
src/cnn/creation.c
View File

@ -9,7 +9,7 @@
#include "include/creation.h"
Network* create_network(int max_size, float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth) {
Network* create_network(int max_size, float learning_rate, int dropout, int initialisation, int input_width, int input_depth) {
if (dropout < 0 || dropout > 100) {
printf_error("La probabilité de dropout n'est pas respecté, elle doit être comprise entre 0 et 100\n");
}
@ -27,19 +27,15 @@ Network* create_network(int max_size, float learning_rate, int dropout, int acti
for (int i=0; i < max_size-1; i++) {
network->kernel[i] = (Kernel*)nalloc(1, sizeof(Kernel));
}
network->kernel[0]->linearisation = DOESNT_LINEARISE;
network->kernel[0]->activation = activation;
network->width[0] = input_width;
network->depth[0] = input_depth;
network->kernel[0]->nn = NULL;
network->kernel[0]->cnn = NULL;
create_a_cube_input_layer(network, 0, input_depth, input_width);
create_a_cube_input_z_layer(network, 0, input_depth, input_width);
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, activation, initialisation, input_width, 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);
@ -51,7 +47,7 @@ Network* create_network_lenet5(float learning_rate, int dropout, int activation,
}
Network* create_network_alexnet(float learning_rate, int dropout, int activation, int initialisation, int size_output) {
Network* network = create_network(12, learning_rate, dropout, activation, initialisation, 227, 3);
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);
@ -67,7 +63,7 @@ Network* create_network_alexnet(float learning_rate, int dropout, int activation
}
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, activation, initialisation, input_width, 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;

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

@ -7,7 +7,7 @@
/*
* 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 activation, int initialisation, int input_width, int input_depth);
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