From 4720fb18e14583d3a1037bcebf695d15a7ff26b6 Mon Sep 17 00:00:00 2001 From: augustin64 Date: Sat, 10 Sep 2022 17:17:49 +0200 Subject: [PATCH] Change network->kernel definition --- src/mnist_cnn/cnn.c | 39 ++++++++-------- src/mnist_cnn/creation.c | 96 ++++++++++++++++++++-------------------- src/mnist_cnn/struct.h | 2 +- 3 files changed, 68 insertions(+), 69 deletions(-) diff --git a/src/mnist_cnn/cnn.c b/src/mnist_cnn/cnn.c index a81879f..a4b0464 100644 --- a/src/mnist_cnn/cnn.c +++ b/src/mnist_cnn/cnn.c @@ -33,32 +33,32 @@ void forward_propagation(Network* network) { int output_dim, output_depth; float*** output; for (int i=0; i < network->size-1; i++) { - if (network->kernel[i].nn==NULL && network->kernel[i].cnn!=NULL) { //CNN + if (network->kernel[i]->nn==NULL && network->kernel[i]->cnn!=NULL) { //CNN output = network->input[i+1]; output_dim = network->dim[i+1][0]; output_depth = network->dim[i+1][1]; - make_convolution(network->input[i], network->kernel[i].cnn, output, output_dim); - choose_apply_function_input(network->kernel[i].activation, output, output_depth, output_dim, output_dim); + make_convolution(network->input[i], network->kernel[i]->cnn, output, output_dim); + choose_apply_function_input(network->kernel[i]->activation, output, output_depth, output_dim, output_dim); } - else if (network->kernel[i].nn!=NULL && network->kernel[i].cnn==NULL) { //NN - 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->kernel[i]->nn!=NULL && network->kernel[i]->cnn==NULL) { //NN + 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 { //Pooling 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]); + 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 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"); + printf("Le réseau ne peut pas contenir deux pooling layers collées"); return; } } @@ -71,7 +71,7 @@ void backward_propagation(Network* network, float wanted_number) { float loss = compute_cross_entropy_loss(network->input[n][0][0], wanted_output, network->dim[n][0]); for (int i=n; i >= 0; i--) { if (i==n) { - if (network->kernel[i].activation == SOFTMAX) { + 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 (int j=0; j < l2; j++) { @@ -79,18 +79,18 @@ void backward_propagation(Network* network, float wanted_number) { } } else { - printf("Erreur, seule la fonction softmax est implémentée pour la dernière couche"); + printf("Erreur, seule la fonction SOFTMAX est implémentée pour la dernière couche"); return; } } else { - if (network->kernel[i].activation == SIGMOID) { + if (network->kernel[i]->activation == SIGMOID) { } - else if (network->kernel[i].activation == TANH) { + else if (network->kernel[i]->activation == TANH) { } - else if (network->kernel[i].activation == RELU) { + else if (network->kernel[i]->activation == RELU) { } } @@ -127,8 +127,7 @@ float* generate_wanted_output(float wanted_number) { } int main() { - Network* network; - network = create_network_lenet5(0, TANH, GLOROT_NORMAL); + Network* network = create_network_lenet5(0, TANH, GLOROT_NORMAL); forward_propagation(network); return 0; } \ No newline at end of file diff --git a/src/mnist_cnn/creation.c b/src/mnist_cnn/creation.c index bf7938d..08148d5 100644 --- a/src/mnist_cnn/creation.c +++ b/src/mnist_cnn/creation.c @@ -14,10 +14,11 @@ Network* create_network(int max_size, int dropout, int initialisation, int input network->initialisation = initialisation; network->size = 1; network->input = (float****)malloc(sizeof(float***)*max_size); - network->kernel = (Kernel*)malloc(sizeof(Kernel)*(max_size-1)); + network->kernel = (Kernel**)malloc(sizeof(Kernel*)*(max_size-1)); network->dim = (int**)malloc(sizeof(int*)*max_size); for (int i=0; i < max_size; i++) { network->dim[i] = (int*)malloc(sizeof(int)*2); + network->kernel[i] = (Kernel*)malloc(sizeof(Kernel)); } network->dim[0][0] = input_dim; network->dim[0][1] = input_depth; @@ -26,8 +27,7 @@ Network* create_network(int max_size, int dropout, int initialisation, int input } Network* create_network_lenet5(int dropout, int activation, int initialisation) { - Network* network; - network = create_network(8, dropout, initialisation, 32, 1); + Network* network = create_network(8, dropout, initialisation, 32, 1); add_convolution(network, 6, 5, activation); add_average_pooling(network, 2, activation); add_convolution(network, 16, 5, activation); @@ -62,9 +62,9 @@ void add_average_pooling(Network* network, int kernel_size, int activation) { printf("Impossible de rajouter une couche d'average pooling, le réseau est déjà plein\n"); return; } - network->kernel[n].cnn = NULL; - network->kernel[n].nn = NULL; - network->kernel[n].activation = activation + 100*kernel_size; + network->kernel[n]->cnn = NULL; + network->kernel[n]->nn = NULL; + network->kernel[n]->activation = activation + 100*kernel_size; create_a_cube_input_layer(network, n, network->dim[n-1][1], network->dim[n-1][0]/2); network->size++; } @@ -75,9 +75,9 @@ void add_average_pooling_flatten(Network* network, int kernel_size, int activati printf("Impossible de rajouter une couche d'average pooling, le réseau est déjà plein\n"); return; } - network->kernel[n].cnn = NULL; - network->kernel[n].nn = NULL; - network->kernel[n].activation = activation + 100*kernel_size; + network->kernel[n]->cnn = NULL; + network->kernel[n]->nn = NULL; + network->kernel[n]->activation = activation + 100*kernel_size; int dim = (network->dim[n-1][0]*network->dim[n-1][0]*network->dim[n-1][1])/(kernel_size*kernel_size); create_a_line_input_layer(network, n, dim); network->size++; @@ -91,43 +91,43 @@ void add_convolution(Network* network, int nb_filter, int kernel_size, int activ } int r = network->dim[n-1][1]; int c = nb_filter; - network->kernel[n].nn = NULL; - network->kernel[n].cnn = (Kernel_cnn*)malloc(sizeof(Kernel_cnn)); - network->kernel[n].activation = activation; - network->kernel[n].cnn->k_size = kernel_size; - network->kernel[n].cnn->rows = r; - network->kernel[n].cnn->columns = c; - network->kernel[n].cnn->w = (float****)malloc(sizeof(float***)*r); - network->kernel[n].cnn->d_w = (float****)malloc(sizeof(float***)*r); + network->kernel[n]->nn = NULL; + network->kernel[n]->cnn = (Kernel_cnn*)malloc(sizeof(Kernel_cnn)); + network->kernel[n]->activation = activation; + network->kernel[n]->cnn->k_size = kernel_size; + network->kernel[n]->cnn->rows = r; + network->kernel[n]->cnn->columns = c; + network->kernel[n]->cnn->w = (float****)malloc(sizeof(float***)*r); + network->kernel[n]->cnn->d_w = (float****)malloc(sizeof(float***)*r); for (int i=0; i < r; i++) { - network->kernel[n].cnn->w[i] = (float***)malloc(sizeof(float**)*c); - network->kernel[n].cnn->d_w[i] = (float***)malloc(sizeof(float**)*c); + network->kernel[n]->cnn->w[i] = (float***)malloc(sizeof(float**)*c); + network->kernel[n]->cnn->d_w[i] = (float***)malloc(sizeof(float**)*c); for (int j=0; j < c; j++) { - network->kernel[n].cnn->w[i][j] = (float**)malloc(sizeof(float*)*kernel_size); - network->kernel[n].cnn->d_w[i][j] = (float**)malloc(sizeof(float*)*kernel_size); + network->kernel[n]->cnn->w[i][j] = (float**)malloc(sizeof(float*)*kernel_size); + network->kernel[n]->cnn->d_w[i][j] = (float**)malloc(sizeof(float*)*kernel_size); for (int k=0; k < kernel_size; k++) { - network->kernel[n].cnn->w[i][j][k] = (float*)malloc(sizeof(float)*kernel_size); - network->kernel[n].cnn->d_w[i][j][k] = (float*)malloc(sizeof(float)*kernel_size); + network->kernel[n]->cnn->w[i][j][k] = (float*)malloc(sizeof(float)*kernel_size); + network->kernel[n]->cnn->d_w[i][j][k] = (float*)malloc(sizeof(float)*kernel_size); } } } - network->kernel[n].cnn->bias = (float***)malloc(sizeof(float**)*c); - network->kernel[n].cnn->d_bias = (float***)malloc(sizeof(float**)*c); + network->kernel[n]->cnn->bias = (float***)malloc(sizeof(float**)*c); + network->kernel[n]->cnn->d_bias = (float***)malloc(sizeof(float**)*c); for (int i=0; i < c; i++) { - network->kernel[n].cnn->bias[i] = (float**)malloc(sizeof(float*)*kernel_size); - network->kernel[n].cnn->d_bias[i] = (float**)malloc(sizeof(float*)*kernel_size); + network->kernel[n]->cnn->bias[i] = (float**)malloc(sizeof(float*)*kernel_size); + network->kernel[n]->cnn->d_bias[i] = (float**)malloc(sizeof(float*)*kernel_size); for (int j=0; j < kernel_size; j++) { - network->kernel[n].cnn->bias[i][j] = (float*)malloc(sizeof(float)*kernel_size); - network->kernel[n].cnn->d_bias[i][j] = (float*)malloc(sizeof(float)*kernel_size); + network->kernel[n]->cnn->bias[i][j] = (float*)malloc(sizeof(float)*kernel_size); + network->kernel[n]->cnn->d_bias[i][j] = (float*)malloc(sizeof(float)*kernel_size); } } create_a_cube_input_layer(network, n, c, network->dim[n-1][0] - 2*(kernel_size/2)); int n_int = network->dim[n-1][0]*network->dim[n-1][0]*network->dim[n-1][1]; int n_out = network->dim[n][0]*network->dim[n][0]*network->dim[n][1]; - initialisation_3d_matrix(network->initialisation, network->kernel[n].cnn->bias, c, kernel_size, kernel_size, n_int+n_out); - initialisation_3d_matrix(ZERO, network->kernel[n].cnn->d_bias, c, kernel_size, kernel_size, n_int+n_out); - initialisation_4d_matrix(network->initialisation, network->kernel[n].cnn->w, r, c, kernel_size, kernel_size, n_int+n_out); - initialisation_4d_matrix(ZERO, network->kernel[n].cnn->d_w, r, c, kernel_size, kernel_size, n_int+n_out); + initialisation_3d_matrix(network->initialisation, network->kernel[n]->cnn->bias, c, kernel_size, kernel_size, n_int+n_out); + initialisation_3d_matrix(ZERO, network->kernel[n]->cnn->d_bias, c, kernel_size, kernel_size, n_int+n_out); + initialisation_4d_matrix(network->initialisation, network->kernel[n]->cnn->w, r, c, kernel_size, kernel_size, n_int+n_out); + initialisation_4d_matrix(ZERO, network->kernel[n]->cnn->d_w, r, c, kernel_size, kernel_size, n_int+n_out); network->size++; } @@ -137,23 +137,23 @@ void add_dense(Network* network, int input_units, int output_units, int activati printf("Impossible de rajouter une couche dense, le réseau est déjà plein\n"); return; } - network->kernel[n].cnn = NULL; - network->kernel[n].nn = (Kernel_nn*)malloc(sizeof(Kernel_nn)); - network->kernel[n].activation = activation; - network->kernel[n].nn->input_units = input_units; - network->kernel[n].nn->output_units = output_units; - network->kernel[n].nn->bias = (float*)malloc(sizeof(float)*output_units); - network->kernel[n].nn->d_bias = (float*)malloc(sizeof(float)*output_units); - network->kernel[n].nn->weights = (float**)malloc(sizeof(float*)*input_units); - network->kernel[n].nn->d_weights = (float**)malloc(sizeof(float*)*input_units); + network->kernel[n]->cnn = NULL; + network->kernel[n]->nn = (Kernel_nn*)malloc(sizeof(Kernel_nn)); + network->kernel[n]->activation = activation; + network->kernel[n]->nn->input_units = input_units; + network->kernel[n]->nn->output_units = output_units; + network->kernel[n]->nn->bias = (float*)malloc(sizeof(float)*output_units); + network->kernel[n]->nn->d_bias = (float*)malloc(sizeof(float)*output_units); + network->kernel[n]->nn->weights = (float**)malloc(sizeof(float*)*input_units); + network->kernel[n]->nn->d_weights = (float**)malloc(sizeof(float*)*input_units); for (int i=0; i < input_units; i++) { - network->kernel[n].nn->weights[i] = (float*)malloc(sizeof(float)*output_units); - network->kernel[n].nn->d_weights[i] = (float*)malloc(sizeof(float)*output_units); + network->kernel[n]->nn->weights[i] = (float*)malloc(sizeof(float)*output_units); + network->kernel[n]->nn->d_weights[i] = (float*)malloc(sizeof(float)*output_units); } - initialisation_1d_matrix(network->initialisation, network->kernel[n].nn->bias, output_units, output_units+input_units); - initialisation_1d_matrix(ZERO, network->kernel[n].nn->d_bias, output_units, output_units+input_units); - initialisation_2d_matrix(network->initialisation, network->kernel[n].nn->weights, input_units, output_units, output_units+input_units); - initialisation_2d_matrix(ZERO, network->kernel[n].nn->d_weights, input_units, output_units, output_units+input_units); + initialisation_1d_matrix(network->initialisation, network->kernel[n]->nn->bias, output_units, output_units+input_units); + initialisation_1d_matrix(ZERO, network->kernel[n]->nn->d_bias, output_units, output_units+input_units); + initialisation_2d_matrix(network->initialisation, network->kernel[n]->nn->weights, input_units, output_units, output_units+input_units); + initialisation_2d_matrix(ZERO, network->kernel[n]->nn->d_weights, input_units, output_units, output_units+input_units); create_a_line_input_layer(network, n, output_units); network->size++; } \ No newline at end of file diff --git a/src/mnist_cnn/struct.h b/src/mnist_cnn/struct.h index 113bf03..4190c62 100644 --- a/src/mnist_cnn/struct.h +++ b/src/mnist_cnn/struct.h @@ -37,7 +37,7 @@ typedef struct Network{ 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; + Kernel** kernel; float**** input; } Network;