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Use of 'linearisation' variable

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julienChemillier 2022-11-04 08:31:58 +01:00
parent 88ff365c70
commit dffc1598ff
2 changed files with 5 additions and 3 deletions
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6
src/cnn/creation.c
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@ -29,8 +29,6 @@ Network* create_network(int max_size, int learning_rate, int dropout, int initia
network->kernel[0]->nn = NULL; network->kernel[0]->nn = NULL;
network->kernel[0]->cnn = NULL; network->kernel[0]->cnn = NULL;
create_a_cube_input_layer(network, 0, input_depth, input_dim); create_a_cube_input_layer(network, 0, input_depth, input_dim);
// create_a_cube_input_z_layer(network, 0, input_depth, input_dim);
// This shouldn't be used (if I'm not mistaken) so to save space, we can do:
network->input_z[0] = NULL; // As we don't backpropagate the input network->input_z[0] = NULL; // As we don't backpropagate the input
return network; return network;
} }
@ -105,6 +103,7 @@ void add_2d_average_pooling(Network* network, int dim_output) {
network->kernel[k_pos]->cnn = NULL; network->kernel[k_pos]->cnn = NULL;
network->kernel[k_pos]->nn = NULL; network->kernel[k_pos]->nn = NULL;
network->kernel[k_pos]->activation = 100*kernel_size; // Ne contient pas de fonction d'activation network->kernel[k_pos]->activation = 100*kernel_size; // Ne contient pas de fonction d'activation
network->kernel[k_pos]->linearisation = 0;
create_a_cube_input_layer(network, n, network->depth[n-1], network->width[n-1]/2); create_a_cube_input_layer(network, n, network->depth[n-1], network->width[n-1]/2);
create_a_cube_input_z_layer(network, n, network->depth[n-1], network->width[n-1]/2); // Will it be used ? create_a_cube_input_z_layer(network, n, network->depth[n-1], network->width[n-1]/2); // Will it be used ?
network->size++; network->size++;
@ -124,6 +123,7 @@ void add_convolution(Network* network, int depth_output, int dim_output, int act
int kernel_size = dim_input - dim_output +1; int kernel_size = dim_input - dim_output +1;
network->kernel[k_pos]->nn = NULL; network->kernel[k_pos]->nn = NULL;
network->kernel[k_pos]->activation = activation; network->kernel[k_pos]->activation = activation;
network->kernel[k_pos]->linearisation = 0;
network->kernel[k_pos]->cnn = (Kernel_cnn*)malloc(sizeof(Kernel_cnn)); network->kernel[k_pos]->cnn = (Kernel_cnn*)malloc(sizeof(Kernel_cnn));
Kernel_cnn* cnn = network->kernel[k_pos]->cnn; Kernel_cnn* cnn = network->kernel[k_pos]->cnn;
@ -179,6 +179,7 @@ void add_dense(Network* network, int output_units, int activation) {
network->kernel[k_pos]->nn = (Kernel_nn*)malloc(sizeof(Kernel_nn)); network->kernel[k_pos]->nn = (Kernel_nn*)malloc(sizeof(Kernel_nn));
Kernel_nn* nn = network->kernel[k_pos]->nn; Kernel_nn* nn = network->kernel[k_pos]->nn;
network->kernel[k_pos]->activation = activation; network->kernel[k_pos]->activation = activation;
network->kernel[k_pos]->linearisation = 0;
nn->input_units = input_units; nn->input_units = input_units;
nn->output_units = output_units; nn->output_units = output_units;
nn->bias = (float*)malloc(sizeof(float)*output_units); nn->bias = (float*)malloc(sizeof(float)*output_units);
@ -214,6 +215,7 @@ void add_dense_linearisation(Network* network, int output_units, int activation)
network->kernel[k_pos]->nn = (Kernel_nn*)malloc(sizeof(Kernel_nn)); network->kernel[k_pos]->nn = (Kernel_nn*)malloc(sizeof(Kernel_nn));
Kernel_nn* nn = network->kernel[k_pos]->nn; Kernel_nn* nn = network->kernel[k_pos]->nn;
network->kernel[k_pos]->activation = activation; network->kernel[k_pos]->activation = activation;
network->kernel[k_pos]->linearisation = 1;
nn->input_units = input_units; nn->input_units = input_units;
nn->output_units = output_units; nn->output_units = output_units;

2
src/cnn/include/struct.h
View File

@ -23,7 +23,7 @@ typedef struct Kernel_nn {
typedef struct Kernel { typedef struct Kernel {
Kernel_cnn* cnn; // NULL si ce n'est pas un cnn Kernel_cnn* cnn; // NULL si ce n'est pas un cnn
Kernel_nn* nn; // NULL si ce n'est pas un nn Kernel_nn* nn; // NULL si ce n'est pas un nn
int activation; // Vaut l'activation sauf pour un pooling où il: vaut pooling_size*100 + activation int activation; // Vaut l'activation sauf pour un pooling où il: vaut pooling_size*100
int linearisation; // Vaut 1 si c'est la linéarisation d'une couche, 0 sinon ?? Ajouter dans les autres int linearisation; // Vaut 1 si c'est la linéarisation d'une couche, 0 sinon ?? Ajouter dans les autres
} Kernel; } Kernel;