augustin64/tipe
1
1
Fork 0
mirror of https://github.com/augustin64/projet-tipe synced 2025-02-02 19:39:39 +01:00
Code Issues Packages Projects Releases Wiki Activity

Add VGG16 architecture

Browse Source
This commit is contained in:
julienChemillier 2023-05-14 19:00:40 +02:00
parent de48f11e78
commit 003183d3fd
2 changed files with 40 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

32
src/cnn/creation.c
View File

@ -62,6 +62,38 @@ Network* create_network_alexnet(float learning_rate, int dropout, int activation
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);

10
src/cnn/include/creation.h
View File

@ -15,11 +15,17 @@ 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_width, int input_depth);
/*
* Renvoie un réseau suivante l'architecture AlexNet
* C'est à dire une entrée de 3x227x227 et une sortie de taille 'size_output'
* 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
*/