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Split creation.c & create models.c

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augustin64 2023-05-15 18:23:30 +02:00
parent 13e786d34b
commit 491013713d
4 changed files with 104 additions and 89 deletions
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67
src/cnn/creation.c
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@ -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++) {

22
src/cnn/include/creation.h
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@ -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
*/

29
src/cnn/include/models.h Normal file
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@ -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

75
src/cnn/models.c Normal file
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@ -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;
}