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Merge branch 'julienChemillier:main' into main

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augustin64 2022-10-14 15:46:21 +02:00 committed by GitHub
commit bc5f491f1f
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5 changed files with 55 additions and 8 deletions
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18
src/cnn/cnn.c
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@ -53,11 +53,11 @@ void forward_propagation(Network* network) {
output_width = network->width[i+1];
activation = k_i->activation;
if (k_i->cnn!=NULL) { // Convolution
if (k_i->cnn) { // Convolution
make_convolution(k_i->cnn, input, output, output_width);
choose_apply_function_matrix(activation, output, output_depth, output_width);
}
else if (k_i->nn!=NULL) { // Full connection
else if (k_i->nn) { // Full connection
if (input_depth==1) { // Vecteur -> Vecteur
make_dense(k_i->nn, input[0][0], output[0][0], input_width, output_width);
} else { // Matrice -> vecteur
@ -80,7 +80,7 @@ void backward_propagation(Network* network, float wanted_number) {
printf_warning("Appel de backward_propagation, incomplet\n");
float* wanted_output = generate_wanted_output(wanted_number);
int n = network->size;
float loss = compute_cross_entropy_loss(network->input[n][0][0], wanted_output, network->width[n]);
float loss = compute_mean_squared_error(network->input[n][0][0], wanted_output, network->width[n]);
int activation, input_depth, input_width, output_depth, output_width;
float*** input;
float*** output;
@ -106,6 +106,18 @@ void backward_propagation(Network* network, float wanted_number) {
free(wanted_output);
}
float compute_mean_squared_error(float* output, float* wanted_output, int len) {
if (len==0) {
printf("Erreur MSE: la longueur de la sortie est de 0 -> division par 0 impossible\n");
return 0.;
}
float loss=0.;
for (int i=0; i < len ; i++) {
loss += (output[i]-wanted_output[i])*(output[i]-wanted_output[i]);
}
return loss/len;
}
float compute_cross_entropy_loss(float* output, float* wanted_output, int len) {
float loss=0.;
for (int i=0; i < len ; i++) {

13
src/cnn/creation.c
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@ -105,26 +105,33 @@ void add_convolution(Network* network, int depth_output, int dim_output, int act
cnn->columns = depth_output;
cnn->w = (float****)malloc(sizeof(float***)*depth_input);
cnn->d_w = (float****)malloc(sizeof(float***)*depth_input);
cnn->last_d_w = (float****)malloc(sizeof(float***)*depth_input);
for (int i=0; i < depth_input; i++) {
cnn->w[i] = (float***)malloc(sizeof(float**)*depth_output);
cnn->d_w[i] = (float***)malloc(sizeof(float**)*depth_output);
cnn->last_d_w[i] = (float***)malloc(sizeof(float**)*depth_output);
for (int j=0; j < depth_output; j++) {
cnn->w[i][j] = (float**)malloc(sizeof(float*)*kernel_size);
cnn->d_w[i][j] = (float**)malloc(sizeof(float*)*kernel_size);
cnn->last_d_w[i][j] = (float**)malloc(sizeof(float*)*kernel_size);
for (int k=0; k < kernel_size; k++) {
cnn->w[i][j][k] = (float*)malloc(sizeof(float)*kernel_size);
cnn->d_w[i][j][k] = (float*)malloc(sizeof(float)*kernel_size);
cnn->last_d_w[i][j][k] = (float*)malloc(sizeof(float)*kernel_size);
}
}
}
cnn->bias = (float***)malloc(sizeof(float**)*depth_output);
cnn->d_bias = (float***)malloc(sizeof(float**)*depth_output);
cnn->last_d_bias = (float***)malloc(sizeof(float**)*depth_output);
for (int i=0; i < depth_output; i++) {
cnn->bias[i] = (float**)malloc(sizeof(float*)*bias_size);
cnn->d_bias[i] = (float**)malloc(sizeof(float*)*bias_size);
cnn->last_d_bias[i] = (float**)malloc(sizeof(float*)*bias_size);
for (int j=0; j < bias_size; j++) {
cnn->bias[i][j] = (float*)malloc(sizeof(float)*bias_size);
cnn->d_bias[i][j] = (float*)malloc(sizeof(float)*bias_size);
cnn->last_d_bias[i][j] = (float*)malloc(sizeof(float)*bias_size);
}
}
create_a_cube_input_layer(network, n, depth_output, bias_size);
@ -155,11 +162,14 @@ void add_dense(Network* network, int output_units, int activation) {
nn->output_units = output_units;
nn->bias = (float*)malloc(sizeof(float)*output_units);
nn->d_bias = (float*)malloc(sizeof(float)*output_units);
nn->last_d_bias = (float*)malloc(sizeof(float)*output_units);
nn->weights = (float**)malloc(sizeof(float*)*input_units);
nn->d_weights = (float**)malloc(sizeof(float*)*input_units);
nn->last_d_weights = (float**)malloc(sizeof(float*)*input_units);
for (int i=0; i < input_units; i++) {
nn->weights[i] = (float*)malloc(sizeof(float)*output_units);
nn->d_weights[i] = (float*)malloc(sizeof(float)*output_units);
nn->last_d_weights[i] = (float*)malloc(sizeof(float)*output_units);
}
create_a_line_input_layer(network, n, output_units);
/* Not currently used
@ -190,11 +200,14 @@ void add_dense_linearisation(Network* network, int output_units, int activation)
nn->bias = (float*)malloc(sizeof(float)*output_units);
nn->d_bias = (float*)malloc(sizeof(float)*output_units);
nn->last_d_bias = (float*)malloc(sizeof(float)*output_units);
nn->weights = (float**)malloc(sizeof(float*)*input_units);
nn->d_weights = (float**)malloc(sizeof(float*)*input_units);
nn->last_d_weights = (float**)malloc(sizeof(float*)*input_units);
for (int i=0; i < input_units; i++) {
nn->weights[i] = (float*)malloc(sizeof(float)*output_units);
nn->d_weights[i] = (float*)malloc(sizeof(float)*output_units);
nn->last_d_weights[i] = (float*)malloc(sizeof(float)*output_units);
}
/* Not currently used
initialisation_1d_matrix(network->initialisation, nn->bias, output_units, output_units+input_units);

13
src/cnn/free.c
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@ -33,27 +33,34 @@ void free_convolution(Network* network, int pos) {
for (int j=0; j < bias_size; j++) {
free(k_pos->bias[i][j]);
free(k_pos->d_bias[i][j]);
free(k_pos->last_d_bias[i][j]);
}
free(k_pos->bias[i]);
free(k_pos->d_bias[i]);
free(k_pos->last_d_bias[i]);
}
free(k_pos->bias);
free(k_pos->d_bias);
free(k_pos->last_d_bias);
for (int i=0; i < r; i++) {
for (int j=0; j < c; j++) {
for (int k=0; k < k_size; k++) {
free(k_pos->w[i][j][k]);
free(k_pos->d_w[i][j][k]);
free(k_pos->last_d_w[i][j][k]);
}
free(k_pos->w[i][j]);
free(k_pos->d_w[i][j]);
free(k_pos->last_d_w[i][j]);
}
free(k_pos->w[i]);
free(k_pos->d_w[i]);
free(k_pos->last_d_w[i]);
}
free(k_pos->w);
free(k_pos->d_w);
free(k_pos->last_d_w);
free(k_pos);
}
@ -65,12 +72,15 @@ void free_dense(Network* network, int pos) {
for (int i=0; i < dim; i++) {
free(k_pos->weights[i]);
free(k_pos->d_weights[i]);
free(k_pos->last_d_weights[i]);
}
free(k_pos->weights);
free(k_pos->d_weights);
free(k_pos->last_d_weights);
free(k_pos->bias);
free(k_pos->d_bias);
free(k_pos->last_d_bias);
free(k_pos);
}
@ -82,12 +92,15 @@ void free_dense_linearisation(Network* network, int pos) {
for (int i=0; i < dim; i++) {
free(k_pos->weights[i]);
free(k_pos->d_weights[i]);
free(k_pos->last_d_weights[i]);
}
free(k_pos->weights);
free(k_pos->d_weights);
free(k_pos->last_d_weights);
free(k_pos->bias);
free(k_pos->d_bias);
free(k_pos->last_d_bias);
free(k_pos);
}

13
src/cnn/include/cnn.h
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@ -7,12 +7,12 @@
/*
* Renvoie si oui ou non (1 ou 0) le neurone va être abandonné
*/
int will_be_drop(int dropout_prob); //CHECKED
int will_be_drop(int dropout_prob);
/*
* Écrit une image 28*28 au centre d'un tableau 32*32 et met à 0 le reste
*/
void write_image_in_network_32(int** image, int height, int width, float** input); //CHECKED
void write_image_in_network_32(int** image, int height, int width, float** input);
/*
* Propage en avant le cnn
@ -22,10 +22,15 @@ void forward_propagation(Network* network);
/*
* Propage en arrière le cnn
*/
void backward_propagation(Network* network, float wanted_number); // TODO
void backward_propagation(Network* network, float wanted_number);
/*
* Renvoie l'erreur du réseau neuronal pour une sortie
* Renvoie l'erreur du réseau neuronal pour une sortie (RMS)
*/
float compute_mean_squared_error(float* output, float* wanted_output, int len);
/*
* Renvoie l'erreur du réseau neuronal pour une sortie (CEL)
*/
float compute_cross_entropy_loss(float* output, float* wanted_output, int len);

6
src/cnn/include/struct.h
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@ -7,8 +7,10 @@ typedef struct Kernel_cnn {
int columns; // Depth of the output
float*** bias; // bias[columns][k_size][k_size]
float*** d_bias; // d_bias[columns][k_size][k_size]
float*** last_d_bias; // last_d_bias[columns][k_size][k_size]
float**** w; // w[rows][columns][k_size][k_size]
float**** d_w; // dw[rows][columns][k_size][k_size]
float**** d_w; // d_w[rows][columns][k_size][k_size]
float**** last_d_w; // last_d_w[rows][columns][k_size][k_size]
} Kernel_cnn;
typedef struct Kernel_nn {
@ -16,8 +18,10 @@ typedef struct Kernel_nn {
int output_units; // Nombre d'éléments en sortie
float* bias; // bias[output_units]
float* d_bias; // d_bias[output_units]
float* last_d_bias; // last_d_bias[output_units]
float** weights; // weight[input_units][output_units]
float** d_weights; // d_weights[input_units][output_units]
float** last_d_weights; // last_d_weights[input_units][output_units]
} Kernel_nn;
typedef struct Kernel {