mirror of
https://github.com/augustin64/projet-tipe
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Update comments of neuron.h
This commit is contained in:
Julien Chemillier
parent
a352c02a07
commit
38f850988b
@ -4,7 +4,7 @@
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typedef struct Neuron {
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float* weights; // Liste de tous les poids des arêtes sortants du neurone
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float bias; // Caractérise le bias du neurone
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float z; // Sauvegarde des calculs faits sur le neurone (programmation dynamique)
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float z; // Sauvegarde des calculs faits sur le neurone
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float *back_weights; // Changement des poids sortants lors de la backpropagation
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float *last_back_weights; // Dernier changement de d_poid_sortants
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@ -1,16 +1,638 @@
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#define PADING_INPUT 2
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#include <stdlib.h>
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#include <stdio.h>
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#include <time.h>
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#include <math.h>
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#include <float.h>
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void write_image_in_newtork_32(int** image, int height, int width, float** network) {
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/* Ecrit une image 28*28 au centre d'un tableau 32*32 et met à 0 le reste */
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#include "cnn.h"
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for (int i=0; i < height+2*PADING_INPUT; i++) {
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for (int j=PADING_INPUT; j < width+2*PADING_INPUT; j++) {
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if (i<PADING_INPUT || i>height+PADING_INPUT || j<PADING_INPUT || j>width+PADING_INPUT){
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network[i][j] = 0.;
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#define PADING_INPUT 2 // Augmente les dimensions de l'image d'entrée
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#define RAND_FLT() ((float)rand())/((float)RAND_MAX) // Génère un flotant entre 0 et 1
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// Les dérivées sont l'opposé
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#define TANH 1
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#define SIGMOID 2
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#define RELU 3
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#define SOFTMAX 4
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#define ZERO 0
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#define GLOROT_NORMAL 1
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#define GLOROT_UNIFROM 2
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#define HE_NORMAL 3
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#define HE_UNIFORM 4
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// Penser à mettre srand(time(NULL)); (pour les proba de dropout)
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float max(float a, float b) {
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return a<b?b:a;
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}
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float sigmoid(float x) {
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return 1/(1 + exp(-x));
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}
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float sigmoid_derivative(float x) {
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float tmp = exp(-x);
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return tmp/((1+tmp)*(1+tmp));
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}
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float relu(float x) {
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return max(0, x);
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}
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float relu_derivative(float x) {
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if (x > 0)
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return 1;
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return 0;
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}
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float tanh_(float x) {
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return tanh(x);
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}
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float tanh_derivative(float x) {
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float a = tanh(x);
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return 1 - a*a;
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}
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void apply_softmax_input(float ***input, int depth, int rows, int columns) {
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int i, j, k;
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float m = FLT_MIN;
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float sum=0;
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for (i=0; i<depth; i++) {
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for (j=0; j<rows; j++) {
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for (k=0; k<columns; k++) {
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m = max(m, input[i][j][k]);
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}
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else {
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network[i][j] = (float)image[i][j] / 255.0f;
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}
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}
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for (i=0; i<depth; i++) {
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for (j=0; j<rows; j++) {
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for (k=0; k<columns; k++) {
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input[i][j][k] = exp(m-input[i][j][k]);
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sum += input[i][j][k];
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}
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}
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}
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for (i=0; i<depth; i++) {
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for (j=0; j<rows; j++) {
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for (k=0; k<columns; k++) {
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input[i][j][k] = input[i][j][k]/sum;
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}
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}
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}
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}
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void apply_function_input(float (*f)(float), float*** input, int depth, int rows, int columns) {
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int i, j ,k;
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for (i=0; i<depth; i++) {
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for (j=0; j<rows; j++) {
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for (k=0; k<columns; k++) {
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input[i][j][k] = (*f)(input[i][j][k]);
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}
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}
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}
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}
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void choose_apply_function_input(int activation, float*** input, int depth, int rows, int columns) {
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if (activation == RELU) {
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apply_function_input(relu, input, depth, rows, columns);
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}
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else if (activation == SIGMOID) {
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apply_function_input(sigmoid, input, depth, rows, columns);
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}
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else if (activation == SOFTMAX) {
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apply_softmax_input(input, depth, rows, columns);
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}
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else if (activation == TANH) {
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apply_function_input(tanh_, input, depth, rows, columns);
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}
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else {
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printf("Erreur, fonction d'activation inconnue");
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}
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}
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int will_be_drop(int dropout_prob) {
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/* Renvoie si oui ou non le neurone va être abandonné */
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return (rand() % 100)<dropout_prob;
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}
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Network* create_network(int max_size, int dropout, int initialisation, int input_dim, int input_depth) {
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/* Créé un réseau qui peut contenir max_size couche (dont celle d'input et d'output) */
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if (dropout<0 || dropout>100) {
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printf("Erreur, la probabilité de dropout n'est pas respecté, elle doit être comprise entre 0 et 100\n");
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}
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Network* network = malloc(sizeof(Network));
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network->max_size = max_size;
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network->dropout = dropout;
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network->initialisation = initialisation;
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network->size = 1;
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network->input = malloc(sizeof(float***)*max_size);
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network->kernel = malloc(sizeof(Kernel)*(max_size-1));
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create_a_cube_input_layer(network, 0, input_depth, input_dim);
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int i, j;
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network->dim = malloc(sizeof(int*)*max_size);
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for (i=0; i<max_size; i++) {
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network->dim[i] = malloc(sizeof(int)*2);
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}
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network->dim[0][0] = input_dim;
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network->dim[0][1] = input_depth;
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return network;
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}
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Network* create_network_lenet5(int dropout, int activation, int initialisation) {
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/* Renvoie un réseau suivant l'architecture LeNet5 */
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Network* network;
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network = create_network(8, dropout, initialisation, 32, 1);
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add_convolution(network, 6, 5, activation);
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add_average_pooling(network, 2, activation);
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add_convolution(network, 16, 5, activation);
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add_average_pooling_flatten(network, 2, activation);
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add_dense(network, 120, 84, activation);
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add_dense(network, 84, 10, activation);
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add_dense(network, 10, 10, SOFTMAX);
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return network;
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}
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void create_a_cube_input_layer(Network* network, int pos, int depth, int dim) {
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/* Créé et alloue de la mémoire à une couche de type input cube */
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int i, j;
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network->input[pos] = malloc(sizeof(float**)*depth);
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for (i=0; i<depth; i++) {
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network->input[pos][i] = malloc(sizeof(float*)*dim);
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for (j=0; j<dim; j++) {
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network->input[pos][i][j] = malloc(sizeof(float)*dim);
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}
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}
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network->dim[pos][0] = dim;
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network->dim[pos][1] = depth;
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}
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void create_a_line_input_layer(Network* network, int pos, int dim) {
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/* Créé et alloue de la mémoire à une couche de type ligne */
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int i;
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network->input[pos] = malloc(sizeof(float**));
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network->input[pos][0] = malloc(sizeof(float*));
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network->input[pos][0][0] = malloc(sizeof(float)*dim);
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}
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void initialisation_1d_matrix(int initialisation, float* matrix, int rows, int n) { //NOT FINISHED
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/* Initialise une matrice 1d rows de float en fonction du type d'initialisation */
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int i;
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float lower_bound = -6/sqrt((double)n);
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float distance = -lower_bound-lower_bound;
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for (i=0; i<rows; i++) {
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matrix[i] = lower_bound + RAND_FLT()*distance;
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}
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}
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void initialisation_2d_matrix(int initialisation, float** matrix, int rows, int columns, int n) { //NOT FINISHED
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/* Initialise une matrice 2d rows*columns de float en fonction du type d'initialisation */
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int i, j;
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float lower_bound = -6/sqrt((double)n);
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float distance = -lower_bound-lower_bound;
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for (i=0; i<rows; i++) {
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for (j=0; j<columns; j++) {
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matrix[i][j] = lower_bound + RAND_FLT()*distance;
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}
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}
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}
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void initialisation_3d_matrix(int initialisation, float*** matrix, int depth, int rows, int columns, int n) { //NOT FINISHED
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/* Initialise une matrice 3d depth*dim*columns de float en fonction du type d'initialisation */
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int i, j, k;
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float lower_bound = -6/sqrt((double)n);
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float distance = -lower_bound-lower_bound;
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for (i=0; i<depth; i++) {
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for (j=0; j<rows; j++) {
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for (k=0; k<columns; k++) {
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matrix[i][j][k] = lower_bound + RAND_FLT()*distance;
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}
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}
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}
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}
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void initialisation_4d_matrix(int initialisation, float**** matrix, int rows, int columns, int rows1, int columns1, int n) { //NOT FINISHED
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/* Initialise une matrice 4d rows*columns*rows1*columns1 de float en fonction du type d'initialisation */
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int i, j, k, l;
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float lower_bound = -6/sqrt((double)n);
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float distance = -lower_bound-lower_bound;
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for (i=0; i<rows; i++) {
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for (j=0; j<columns; j++) {
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for (k=0; k<rows1; k++) {
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for (l=0; l<columns1; l++) {
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matrix[i][j][k][l] = lower_bound + RAND_FLT()*distance;
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}
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}
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}
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}
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}
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void add_average_pooling(Network* network, int kernel_size, int activation) {
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/* Ajoute au réseau une couche d'average pooling valide de dimension dim*dim */
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int n = network->size;
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if (network->max_size == n) {
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printf("Impossible de rajouter une couche d'average pooling, le réseau est déjà plein\n");
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return;
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}
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network->kernel[n].cnn = NULL;
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network->kernel[n].nn = NULL;
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network->kernel[n].activation = activation + 100*kernel_size;
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create_a_cube_input_layer(network, n, network->dim[n-1][1], network->dim[n-1][0]/2);
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network->size++;
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}
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void add_average_pooling_flatten(Network* network, int kernel_size, int activation) {
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/* Ajoute au réseau une couche d'average pooling valide de dimension dim*dim qui aplatit */
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int n = network->size;
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if (network->max_size == n) {
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printf("Impossible de rajouter une couche d'average pooling, le réseau est déjà plein\n");
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return;
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}
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network->kernel[n].cnn = NULL;
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network->kernel[n].nn = NULL;
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network->kernel[n].activation = activation + 100*kernel_size;
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int dim = (network->dim[n-1][0]*network->dim[n-1][0]*network->dim[n-1][1])/(kernel_size*kernel_size);
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create_a_line_input_layer(network, n, dim);
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network->size++;
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}
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void add_convolution(Network* network, int nb_filter, int kernel_size, int activation) {
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/* Ajoute une couche de convolution dim*dim au réseau et initialise les kernels */
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int n = network->size, i, j, k;
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if (network->max_size == n) {
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printf("Impossible de rajouter une couche de convolution, le réseau est déjà plein\n");
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return;
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}
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int r = network->dim[n-1][1];
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int c = nb_filter;
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network->kernel[n].nn = NULL;
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network->kernel[n].cnn = malloc(sizeof(Kernel_cnn));
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network->kernel[n].activation = activation;
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network->kernel[n].cnn->k_size = kernel_size;
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network->kernel[n].cnn->rows = r;
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network->kernel[n].cnn->columns = c;
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network->kernel[n].cnn->w = malloc(sizeof(float***)*r);
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network->kernel[n].cnn->d_w = malloc(sizeof(float***)*r);
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for (i=0; i<r; i++) {
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network->kernel[n].cnn->w[i] = malloc(sizeof(float**)*c);
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network->kernel[n].cnn->d_w[i] = malloc(sizeof(float**)*c);
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for (j=0; j<c; j++) {
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network->kernel[n].cnn->w[i][j] = malloc(sizeof(float*)*kernel_size);
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network->kernel[n].cnn->d_w[i][j] = malloc(sizeof(float*)*kernel_size);
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for (k=0; k<kernel_size; k++) {
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network->kernel[n].cnn->w[i][j][k] = malloc(sizeof(float)*kernel_size);
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network->kernel[n].cnn->d_w[i][j][k] = malloc(sizeof(float)*kernel_size);
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}
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}
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}
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network->kernel[n].cnn->bias = malloc(sizeof(float**)*c);
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network->kernel[n].cnn->d_bias = malloc(sizeof(float**)*c);
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for (i=0; i<c; i++) {
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network->kernel[n].cnn->bias[i] = malloc(sizeof(float*)*kernel_size);
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network->kernel[n].cnn->d_bias[i] = malloc(sizeof(float*)*kernel_size);
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for (j=0; j<kernel_size; j++) {
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network->kernel[n].cnn->bias[i][j] = malloc(sizeof(float)*kernel_size);
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network->kernel[n].cnn->d_bias[i][j] = malloc(sizeof(float)*kernel_size);
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}
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}
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create_a_cube_input_layer(network, n, c, network->dim[n-1][0] - 2*(kernel_size/2));
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int n_int = network->dim[n-1][0]*network->dim[n-1][0]*network->dim[n-1][1];
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int n_out = network->dim[n][0]*network->dim[n][0]*network->dim[n][1];
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initialisation_3d_matrix(network->initialisation, network->kernel[n].cnn->bias, c, kernel_size, kernel_size, n_int+n_out);
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initialisation_3d_matrix(ZERO, network->kernel[n].cnn->d_bias, c, kernel_size, kernel_size, n_int+n_out);
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initialisation_4d_matrix(network->initialisation, network->kernel[n].cnn->w, r, c, kernel_size, kernel_size, n_int+n_out);
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initialisation_4d_matrix(ZERO, network->kernel[n].cnn->d_w, r, c, kernel_size, kernel_size, n_int+n_out);
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network->size++;
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}
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void add_dense(Network* network, int input_units, int output_units, int activation) {
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/* Ajoute une couche dense au réseau et initialise les poids et les biais*/
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int n = network->size;
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if (network->max_size == n) {
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printf("Impossible de rajouter une couche dense, le réseau est déjà plein\n");
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return;
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}
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network->kernel[n].cnn = NULL;
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network->kernel[n].nn = malloc(sizeof(Kernel_nn));
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network->kernel[n].activation = activation;
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network->kernel[n].nn->input_units = input_units;
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network->kernel[n].nn->output_units = output_units;
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network->kernel[n].nn->bias = malloc(sizeof(float)*output_units);
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network->kernel[n].nn->d_bias = malloc(sizeof(float)*output_units);
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network->kernel[n].nn->weights = malloc(sizeof(float*)*input_units);
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network->kernel[n].nn->d_weights = malloc(sizeof(float*)*input_units);
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for (int i=0; i<input_units; i++) {
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network->kernel[n].nn->weights[i] = malloc(sizeof(float)*output_units);
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network->kernel[n].nn->d_weights[i] = malloc(sizeof(float)*output_units);
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}
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initialisation_1d_matrix(network->initialisation, network->kernel[n].nn->bias, output_units, output_units+input_units);
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initialisation_1d_matrix(ZERO, network->kernel[n].nn->d_bias, output_units, output_units+input_units);
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initialisation_2d_matrix(network->initialisation, network->kernel[n].nn->weights, input_units, output_units, output_units+input_units);
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initialisation_2d_matrix(ZERO, network->kernel[n].nn->d_weights, input_units, output_units, output_units+input_units);
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create_a_line_input_layer(network, n, output_units);
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network->size++;
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}
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void write_image_in_newtork_32(int** image, int height, int width, float** input) {
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/* Ecrit une image 28*28 au centre d'un tableau 32*32 et met à 0 le reste */
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for (int i=0; i < height+2*PADING_INPUT; i++) {
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for (int j=PADING_INPUT; j < width+2*PADING_INPUT; j++) {
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if (i<PADING_INPUT || i>height+PADING_INPUT || j<PADING_INPUT || j>width+PADING_INPUT) {
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input[i][j] = 0.;
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}
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else {
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input[i][j] = (float)image[i][j] / 255.0f;
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}
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}
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}
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}
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void make_convolution(float*** input, Kernel_cnn* kernel, float*** output, int output_dim) {
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/* Effectue une convolution sans stride */
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//NOT FINISHED, MISS CONDITIONS ON THE CONVOLUTION
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float f;
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int i, j, k, a, b, c, n=kernel->k_size;
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for (i=0; i<kernel->columns; i++) {
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for (j=0; j<output_dim; j++) {
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for (k=0; k<output_dim; k++) {
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f = kernel->bias[i][j][k];
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for (a=0; a<kernel->rows; a++) {
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for (b=0; b<n; b++) {
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for (c=0; c<n; c++) {
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f += kernel->w[a][i][b][c]*input[a][j+a][k+b];
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}
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}
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}
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output[i][j][k] = f;
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}
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}
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}
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}
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|
||||
void make_average_pooling(float*** input, float*** output, int size, int output_depth, int output_dim) {
|
||||
/* Effecute un average pooling avec stride=size */
|
||||
//NOT FINISHED, MISS CONDITIONS ON THE POOLING
|
||||
float average;
|
||||
int i, j, k, a, b, n=size*size;
|
||||
for (i=0; i<output_depth; i++) {
|
||||
for (j=0; j<output_dim; j++) {
|
||||
for (k=0; k<output_dim; k++) {
|
||||
average = 0.;
|
||||
for (a=0; a<size; a++) {
|
||||
for (b=0; b<size; b++) {
|
||||
average += input[i][2*j +a][2*k +b];
|
||||
}
|
||||
}
|
||||
output[i][j][k] = average;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void make_average_pooling_flattened(float*** input, float* output, int size, int input_depth, int input_dim) {
|
||||
/* Effectue un average pooling avec stride=size et aplatissement */
|
||||
if ((input_depth*input_dim*input_dim) % (size*size) != 0) {
|
||||
printf("Erreur, deux layers non compatibles avec un average pooling flattened");
|
||||
return;
|
||||
}
|
||||
float average;
|
||||
int i, j, k, a, b, n=size*size, cpt=0;
|
||||
int output_dim = input_dim - 2*(size/2);
|
||||
for (i=0; i<input_depth; i++) {
|
||||
for (j=0; j<output_dim; j++) {
|
||||
for (k=0; k<output_dim; k++) {
|
||||
average = 0.;
|
||||
for (a=0; a<size; a++) {
|
||||
for (b=0; b<size; b++) {
|
||||
average += input[i][2*j +a][2*k +b];
|
||||
}
|
||||
}
|
||||
output[cpt] = average;
|
||||
cpt++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void make_fully_connected(float* input, Kernel_nn* kernel, float* output, int size_input, int size_output) {
|
||||
/* Effecute une full connection */
|
||||
int i, j, k;
|
||||
float f;
|
||||
for (i=0; i<size_output; i++) {
|
||||
f = kernel->bias[i];
|
||||
for (j=0; j<size_input; j++) {
|
||||
f += kernel->weights[i][j]*input[j];
|
||||
}
|
||||
output[i] = f;
|
||||
}
|
||||
}
|
||||
|
||||
void free_a_cube_input_layer(Network* network, int pos, int depth, int dim) {
|
||||
/* Libère la mémoire allouée à une couche de type input cube */
|
||||
int i, j, k;
|
||||
for (i=0; i<depth; i++) {
|
||||
for (j=0; j<dim; j++) {
|
||||
free(network->input[pos][i][j]);
|
||||
}
|
||||
free(network->input[pos][i]);
|
||||
}
|
||||
free(network->input[pos]);
|
||||
}
|
||||
|
||||
void free_a_line_input_layer(Network* network, int pos) {
|
||||
/* Libère la mémoire allouée à une couche de type input line */
|
||||
free(network->input[pos][0][0]);
|
||||
free(network->input[pos][0]);
|
||||
free(network->input[pos]);
|
||||
}
|
||||
|
||||
void free_average_pooling(Network* network, int pos) {
|
||||
/* Libère l'espace mémoie et supprime une couche d'average pooling classique */
|
||||
free_a_cube_input_layer(network, pos, network->dim[pos-1][1], network->dim[pos-1][0]/2);
|
||||
}
|
||||
|
||||
void free_average_pooling_flatten(Network* network, int pos) {
|
||||
/* Libère l'espace mémoie et supprime une couche d'average pooling flatten */
|
||||
free_a_line_input_layer(network, pos);
|
||||
}
|
||||
|
||||
void free_convolution(Network* network, int pos) {
|
||||
/* Libère l'espace mémoire et supprime une couche de convolution */
|
||||
int i, j, k, c = network->kernel[pos].cnn->columns;
|
||||
int k_size = network->kernel[pos].cnn->k_size;
|
||||
int r = network->kernel[pos].cnn->rows;
|
||||
free_a_cube_input_layer(network, pos, c, network->dim[pos-1][0] - 2*(k_size/2));
|
||||
for (i=0; i<c; i++) {
|
||||
for (j=0; j<k_size; j++) {
|
||||
free(network->kernel[pos].cnn->bias[i][j]);
|
||||
free(network->kernel[pos].cnn->d_bias[i][j]);
|
||||
}
|
||||
free(network->kernel[pos].cnn->bias[i]);
|
||||
free(network->kernel[pos].cnn->d_bias[i]);
|
||||
}
|
||||
free(network->kernel[pos].cnn->bias);
|
||||
free(network->kernel[pos].cnn->d_bias);
|
||||
|
||||
for (i=0; i<r; i++) {
|
||||
for (j=0; j<c; j++) {
|
||||
for (k=0; k<k_size; k++) {
|
||||
free(network->kernel[pos].cnn->w[i][j][k]);
|
||||
free(network->kernel[pos].cnn->d_w[i][j][k]);
|
||||
}
|
||||
free(network->kernel[pos].cnn->w[i][j]);
|
||||
free(network->kernel[pos].cnn->d_w[i][j]);
|
||||
}
|
||||
free(network->kernel[pos].cnn->w[i]);
|
||||
free(network->kernel[pos].cnn->d_w[i]);
|
||||
}
|
||||
free(network->kernel[pos].cnn->w);
|
||||
free(network->kernel[pos].cnn->d_w);
|
||||
|
||||
free(network->kernel[pos].cnn);
|
||||
}
|
||||
|
||||
void free_dense(Network* network, int pos) {
|
||||
/* Libère l'espace mémoire et supprime une couche dense */
|
||||
free_a_line_input_layer(network, pos);
|
||||
int i, dim = network->kernel[pos].nn->output_units;
|
||||
for (int i=0; i<dim; i++) {
|
||||
free(network->kernel[pos].nn->weights[i]);
|
||||
free(network->kernel[pos].nn->d_weights[i]);
|
||||
}
|
||||
free(network->kernel[pos].nn->weights);
|
||||
free(network->kernel[pos].nn->d_weights);
|
||||
|
||||
free(network->kernel[pos].nn->bias);
|
||||
free(network->kernel[pos].nn->d_bias);
|
||||
|
||||
free(network->kernel[pos].nn);
|
||||
}
|
||||
|
||||
void free_network_creation(Network* network) {
|
||||
/* Libère l'espace alloué dans la fonction 'create_network' */
|
||||
free_a_cube_input_layer(network, 0, network->dim[0][1], network->dim[0][0]);
|
||||
|
||||
for (int i=0; i<network->max_size; i++) {
|
||||
free(network->dim[i]);
|
||||
}
|
||||
free(network->dim);
|
||||
|
||||
free(network->kernel);
|
||||
free(network->input);
|
||||
|
||||
free(network);
|
||||
}
|
||||
|
||||
void free_network_lenet5(Network* network) {
|
||||
/* Libère l'espace alloué dans la fonction 'create_network_lenet5' */
|
||||
free_dense(network, 6);
|
||||
free_dense(network, 5);
|
||||
free_dense(network, 4);
|
||||
free_average_pooling_flatten(network, 3);
|
||||
free_convolution(network, 2);
|
||||
free_average_pooling(network, 1);
|
||||
free_convolution(network, 0);
|
||||
free_network_creation(network);
|
||||
if (network->size != network->max_size) {
|
||||
printf("Attention, le réseau LeNet5 n'est pas complet");
|
||||
}
|
||||
}
|
||||
|
||||
void forward_propagation(Network* network) {
|
||||
/* Propage en avant le cnn */
|
||||
for (int i=0; i < network->size-1; i++) {
|
||||
if (network->kernel[i].nn==NULL && network->kernel[i].cnn!=NULL) {
|
||||
make_convolution(network->input[i], network->kernel[i].cnn, network->input[i+1], network->dim[i+1][0]);
|
||||
choose_apply_function_input(network->kernel[i].activation, network->input[i+1], network->dim[i+1][1], network->dim[i+1][0], network->dim[i+1][0]);
|
||||
}
|
||||
else if (network->kernel[i].nn!=NULL && network->kernel[i].cnn==NULL) {
|
||||
make_fully_connected(network->input[i][0][0], network->kernel[i].nn, network->input[i+1][0][0], network->dim[i][0], network->dim[i+1][0]);
|
||||
choose_apply_function_input(network->kernel[i].activation, network->input[i+1], 1, 1, network->dim[i+1][0]);
|
||||
}
|
||||
else {
|
||||
if (network->size-2==i) {
|
||||
printf("Le réseau ne peut pas finir par une pooling layer");
|
||||
return;
|
||||
}
|
||||
if (network->kernel[i+1].nn!=NULL && network->kernel[i+1].cnn==NULL) {
|
||||
make_average_pooling_flattened(network->input[i], network->input[i+1][0][0], network->kernel[i].activation/100, network->dim[i][1], network->dim[i][0]);
|
||||
choose_apply_function_input(network->kernel[i].activation%100, network->input[i+1], 1, 1, network->dim[i+1][0]);
|
||||
}
|
||||
else if (network->kernel[i+1].nn==NULL && network->kernel[i+1].cnn!=NULL) {
|
||||
make_average_pooling(network->input[i], network->input[i+1], network->kernel[i].activation/100, network->dim[i+1][1], network->dim[i+1][0]);
|
||||
choose_apply_function_input(network->kernel[i].activation%100, network->input[i+1], network->dim[i+1][1], network->dim[i+1][0], network->dim[i+1][0]);
|
||||
}
|
||||
else {
|
||||
printf("Le réseau ne peut pas contenir deux poolings layers collées");
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void backward_propagation(Network* network, float wanted_number) {
|
||||
/* Propage en arrière le cnn */
|
||||
float* wanted_output = generate_wanted_output(wanted_number);
|
||||
int n = network->size-1;
|
||||
float loss = compute_cross_entropy_loss(network->input[n][0][0], wanted_output, network->dim[n][0]);
|
||||
int i, j;
|
||||
for (i=n; i>=0; i--) {
|
||||
if (i==n) {
|
||||
if (network->kernel[i].activation == SOFTMAX) {
|
||||
int l2 = network->dim[i][0]; // Taille de la dernière couche
|
||||
int l1 = network->dim[i-1][0];
|
||||
for (j=0; j<l2; j++) {
|
||||
|
||||
}
|
||||
}
|
||||
else {
|
||||
printf("Erreur, seule la fonction softmax est implémentée pour la dernière couche");
|
||||
return;
|
||||
}
|
||||
}
|
||||
else {
|
||||
if (network->kernel[i].activation == SIGMOID) {
|
||||
|
||||
}
|
||||
else if (network->kernel[i].activation == TANH) {
|
||||
|
||||
}
|
||||
else if (network->kernel[i].activation == RELU) {
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
free(wanted_output);
|
||||
}
|
||||
|
||||
float compute_cross_entropy_loss(float* output, float* wanted_output, int len) {
|
||||
/* Renvoie l'erreur du réseau neuronal pour une sortie */
|
||||
float loss=0.;
|
||||
for (int i=0; i<len ; i++) {
|
||||
if (wanted_output[i]==1) {
|
||||
if (output[i]==0.) {
|
||||
loss -= log(FLT_EPSILON);
|
||||
}
|
||||
else {
|
||||
loss -= log(output[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
return loss;
|
||||
}
|
||||
|
||||
float* generate_wanted_output(float wanted_number) {
|
||||
/* On considère que la sortie voulue comporte 10 éléments */
|
||||
float* wanted_output = malloc(sizeof(float)*10);
|
||||
for (int i=0; i<10; i++) {
|
||||
if (i==wanted_number) {
|
||||
wanted_output[i]=1;
|
||||
}
|
||||
else {
|
||||
wanted_output[i]=0;
|
||||
}
|
||||
}
|
||||
return wanted_output;
|
||||
}
|
||||
|
||||
94
src/mnist_cnn/cnn.h
Normal file
94
src/mnist_cnn/cnn.h
Normal file
@ -0,0 +1,94 @@
|
||||
#include <stdlib.h>
|
||||
#include <stdio.h>
|
||||
#include <time.h>
|
||||
#include <math.h>
|
||||
#include <float.h>
|
||||
|
||||
#ifndef DEF_CNN_H
|
||||
#define DEF_CNN_H
|
||||
|
||||
|
||||
typedef struct Kernel_cnn {
|
||||
int k_size;
|
||||
int rows;
|
||||
int columns;
|
||||
int b;
|
||||
float*** bias; // De dimension columns*k_size*k_size
|
||||
float*** d_bias; // De dimension columns*k_size*k_size
|
||||
float**** w; // De dimension rows*columns*k_size*k_size
|
||||
float**** d_w; // De dimension rows*columns*k_size*k_size
|
||||
} Kernel_cnn;
|
||||
|
||||
typedef struct Kernel_nn {
|
||||
int input_units;
|
||||
int output_units;
|
||||
float* bias; // De dimension output_units
|
||||
float* d_bias; // De dimension output_units
|
||||
float** weights; // De dimension input_units*output_units
|
||||
float** d_weights; // De dimension input_units*output_units
|
||||
} Kernel_nn;
|
||||
|
||||
typedef struct Kernel {
|
||||
Kernel_cnn* cnn;
|
||||
Kernel_nn* nn;
|
||||
int activation; // Vaut l'activation sauf pour un pooling où il: vaut kernel_size*100 + activation
|
||||
} Kernel;
|
||||
|
||||
typedef struct Layer {
|
||||
|
||||
} Layer;
|
||||
|
||||
typedef struct Network{
|
||||
int dropout; // Contient la probabilité d'abandon entre 0 et 100 (inclus)
|
||||
int initialisation; // Contient le type d'initialisation
|
||||
int max_size; // Taille maximale du réseau après initialisation
|
||||
int size; // Taille actuelle du réseau
|
||||
int** dim; // Contient les dimensions de l'input (width*depth)
|
||||
Kernel* kernel;
|
||||
float**** input;
|
||||
} Network;
|
||||
|
||||
float max(float a, float b);
|
||||
float sigmoid(float x);
|
||||
float sigmoid_derivative(float x);
|
||||
float relu(float x);
|
||||
float relu_derivative(float x);
|
||||
float tanh_(float x);
|
||||
float tanh_derivative(float x);
|
||||
void apply_softmax_input(float ***input, int depth, int rows, int columns);
|
||||
void apply_function_input(float (*f)(float), float*** input, int depth, int rows, int columns);
|
||||
void choose_apply_function_input(int activation, float*** input, int depth, int rows, int columns);
|
||||
int will_be_drop(int dropout_prob);
|
||||
Network* create_network(int max_size, int dropout, int initialisation, int input_dim, int input_depth);
|
||||
Network* create_network_lenet5(int dropout, int activation, int initialisation);
|
||||
void create_a_cube_input_layer(Network* network, int pos, int depth, int dim);
|
||||
void create_a_line_input_layer(Network* network, int pos, int dim);
|
||||
void initialisation_1d_matrix(int initialisation, float* matrix, int rows, int n); //NOT FINISHED (UNIFORM AND VARIATIONS)
|
||||
void initialisation_2d_matrix(int initialisation, float** matrix, int rows, int columns, int n); //NOT FINISHED
|
||||
void initialisation_3d_matrix(int initialisation, float*** matrix, int depth, int rows, int columns, int n); //NOT FINISHED
|
||||
void initialisation_4d_matrix(int initialisation, float**** matrix, int rows, int columns, int rows1, int columns1, int n); //NOT FINISHED
|
||||
void add_average_pooling(Network* network, int kernel_size, int activation);
|
||||
void add_average_pooling_flatten(Network* network, int kernel_size, int activation);
|
||||
void add_convolution(Network* network, int nb_filter, int kernel_size, int activation);
|
||||
void add_dense(Network* network, int input_units, int output_units, int activation);
|
||||
void write_image_in_newtork_32(int** image, int height, int width, float** input);
|
||||
void make_convolution(float*** input, Kernel_cnn* kernel, float*** output, int output_dim);
|
||||
void make_average_pooling(float*** input, float*** output, int size, int output_depth, int output_dim);
|
||||
void make_average_pooling_flattened(float*** input, float* output, int size, int input_depth, int input_dim);
|
||||
void make_fully_connected(float* input, Kernel_nn* kernel, float* output, int size_input, int size_output);
|
||||
void free_a_cube_input_layer(Network* network, int pos, int depth, int dim);
|
||||
void free_a_line_input_layer(Network* network, int pos);
|
||||
void free_average_pooling(Network* network, int pos);
|
||||
void free_average_pooling_flatten(Network* network, int pos);
|
||||
void free_convolution(Network* network, int pos);
|
||||
void free_dense(Network* network, int pos);
|
||||
void free_network_creation(Network* network);
|
||||
void free_network_lenet5(Network* network);
|
||||
float compute_cross_entropy_loss(float* output, float* wanted_output, int len);
|
||||
void forward_propagation(Network* network);
|
||||
void backward_propagation(Network* network, float wanted_number); //NOT FINISHED
|
||||
float compute_cross_entropy_loss(float* output, float* wanted_output, int len);
|
||||
float* generate_wanted_output(float wanted_number);
|
||||
|
||||
|
||||
#endif
|
||||
127
src/test.c
Normal file
127
src/test.c
Normal file
@ -0,0 +1,127 @@
|
||||
#include <stdlib.h>
|
||||
#include <stdio.h>
|
||||
#include <time.h>
|
||||
typedef struct Neuron{
|
||||
float bias; // Caractérise le bias du neurone
|
||||
float z; // Sauvegarde des calculs faits sur le neurone (programmation dynamique)
|
||||
|
||||
float back_bias; // Changement du bias lors de la backpropagation
|
||||
float last_back_bias; // Dernier changement de back_bias
|
||||
} Neuron;
|
||||
|
||||
typedef struct Bias{
|
||||
float bias;
|
||||
float back_bias;
|
||||
float last_back_bias;
|
||||
} Bias;
|
||||
|
||||
typedef struct Matrix {
|
||||
int rows; // Nombre de lignes de la matrice
|
||||
int columns; // Nombre de colonnes de la matrice
|
||||
float** value; // Tableau 2d comportant les valeurs de matrice
|
||||
} Matrix;
|
||||
|
||||
typedef struct Matrix_of_neurons {
|
||||
int rows; // Nombre de lignes de la matrice
|
||||
int columns; // Nombre de colonnes de la matrice
|
||||
float** Neuron; // Tableau 2d comportant les valeurs de matrice
|
||||
} Matrix_of_neurons;
|
||||
|
||||
typedef struct Matrix_of_bias {
|
||||
int rows;
|
||||
int columns;
|
||||
float*** bias;
|
||||
} Matrix_of_bias;
|
||||
|
||||
typedef struct Layer {
|
||||
int rows; // Nombre de matrices du tableau de neurones
|
||||
Matrix** conv; // Tableau de matrices des neurones dans la couche
|
||||
} Layer;
|
||||
|
||||
typedef struct Filter {
|
||||
int columns;
|
||||
int rows;
|
||||
int dim_bias;
|
||||
Matrix_of_neurons*** kernel; // De dimension columns*rows
|
||||
Matrix_of_bias** bias; // De dimension columns
|
||||
} Filter;
|
||||
|
||||
typedef struct Network{
|
||||
int dropout; // Contains the probability of dropout bewteen 0 and 100
|
||||
int max_size;
|
||||
int size; // Taille total du réseau
|
||||
int size_cnn; // Nombre de couches dans le cnn
|
||||
int* type_kernel; //De taille size -1
|
||||
|
||||
Layer** input; // Tableau des couches dans le réseau neuronal
|
||||
Filter** kernel;
|
||||
} Network;
|
||||
|
||||
void write_image_in_newtork_32(int** image, int height, int width, float** network) {
|
||||
/* Ecrit une image 28*28 au centre d'un tableau 32*32 et met à 0 le reste */
|
||||
|
||||
for (int i=0; i < height+2*PADING_INPUT; i++) {
|
||||
for (int j=PADING_INPUT; j < width+2*PADING_INPUT; j++) {
|
||||
if (i<PADING_INPUT || i>height+PADING_INPUT || j<PADING_INPUT || j>width+PADING_INPUT){
|
||||
network[i][j] = 0.;
|
||||
}
|
||||
else {
|
||||
network[i][j] = (float)image[i][j] / 255.0f;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
void make_convolution(Layer* input, Filter* filter, Layer* output){
|
||||
/* Effectue une convolution sans stride */
|
||||
if (filter->columns != output->rows) {
|
||||
printf("Erreur, le filtre de la convolution et la sortie ne sont pas compatibles");
|
||||
return;
|
||||
}
|
||||
if (filter->dim_bias != output->rows) {
|
||||
printf("Erreur, le biais et la sortie de la convolution n'ont pas les mêmes dimensions");
|
||||
return;
|
||||
}
|
||||
|
||||
// MISS CONDITIONS ON THE CONVOLUTION
|
||||
int i, j, k;
|
||||
for (i=0; i < filter->rows; i++) {
|
||||
for (j=0; j < filter->dim_bias; j++) {
|
||||
for (int k=0; k < filter->dim_bias; k++) {
|
||||
//output->conv[j][k] = filter->bias[i]->bias
|
||||
// COPY BIAS OF FILTERS IN OUTPUT
|
||||
// POUR CHAQUE COLONNE DANS LE KERNEL
|
||||
// ON APPLIQUE LE FILTRE SUR CHAQUE LIGNE DE L'INPUT ET LES SOMMES
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void make_average_pooling(Layer* input, int dim_pooling, Layer* output){
|
||||
/* Effectue un average pooling avec full strides */
|
||||
|
||||
if (input->rows != output->rows || output->conv[0]->rows*dim_pooling != input->conv[0]->rows || input->rows != output->rows) {
|
||||
printf("Erreur, dimension de la sortie et de l'entrée ne sont pas compatibles avec l'average pooling");
|
||||
return;
|
||||
}
|
||||
int i, j, k, a, b, nb=dim_pooling*dim_pooling;
|
||||
for (i=0; i < input->rows; i++) {
|
||||
for (j=0; j < output->conv[i]->rows; j++) {
|
||||
for (k=0; k < output->conv[i]->columns; k++) {
|
||||
output->conv[i]->value[j][k] = 0;
|
||||
for (a=0; a < dim_pooling; a++) {
|
||||
for (b=0; b < dim_pooling; b++) {
|
||||
output->conv[i]->value[j][k] += input->conv[i]->value[dim_pooling*j + a][dim_pooling*k + b];
|
||||
}
|
||||
}
|
||||
output->conv[i]->value[j][k] /= nb;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void forward_propagation_cnn() {
|
||||
/* Effectue une forward propagation d'un cnn */
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user