From e61898963282b5e403322b9c2f90b061e212a748 Mon Sep 17 00:00:00 2001 From: Julien Chemillier Date: Sat, 8 Oct 2022 14:13:02 +0200 Subject: [PATCH] Modification in the structure --- src/cnn/creation.c | 13 +++++++++++++ src/cnn/free.c | 13 +++++++++++++ src/cnn/include/struct.h | 6 +++++- 3 files changed, 31 insertions(+), 1 deletion(-) diff --git a/src/cnn/creation.c b/src/cnn/creation.c index 9ca3cba..93d851a 100644 --- a/src/cnn/creation.c +++ b/src/cnn/creation.c @@ -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); diff --git a/src/cnn/free.c b/src/cnn/free.c index 600c969..e2870b6 100644 --- a/src/cnn/free.c +++ b/src/cnn/free.c @@ -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); } diff --git a/src/cnn/include/struct.h b/src/cnn/include/struct.h index 4ac7c1e..47ecd6b 100644 --- a/src/cnn/include/struct.h +++ b/src/cnn/include/struct.h @@ -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 {