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Add copy_network and patch_network

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augustin64 2022-05-14 10:28:40 +02:00
parent 6532ad2545
commit ce947fe91d
2 changed files with 84 additions and 22 deletions
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16
src/mnist/include/neural_network.h
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@ -15,13 +15,15 @@ float sigmoid(float x);
float sigmoid_derivative(float x); float sigmoid_derivative(float x);
float leaky_ReLU(float x); float leaky_ReLU(float x);
float leaky_ReLU_derivative(float x); float leaky_ReLU_derivative(float x);
void network_creation(Network* network_neuronal, int* neurons_per_layer, int nb_layers); void network_creation(Network* network, int* neurons_per_layer, int nb_layers);
void deletion_of_network(Network* network_neuronal); void deletion_of_network(Network* network);
void forward_propagation(Network* network_neuronal); void forward_propagation(Network* network);
int* desired_output_creation(Network* network_neuronal, int wanted_number); int* desired_output_creation(Network* network, int wanted_number);
void backward_propagation(Network* network_neuronal, int* desired_output); void backward_propagation(Network* network, int* desired_output);
void network_modification(Network* network_neuronal, uint32_t nb_modifs); void network_modification(Network* network, uint32_t nb_modifs);
void network_initialisation(Network* network_neuronal); void network_initialisation(Network* network);
void patch_network(Network* network, Network* delta, uint32_t nb_modifs);
Network* copy_network(Network* network);
float loss_computing(Network* network, int numero_voulu); float loss_computing(Network* network, int numero_voulu);
#endif #endif

88
src/mnist/neural_network.c
View File

@ -92,6 +92,7 @@ void deletion_of_network(Network* network) {
} }
free(layer->neurons); // On libère enfin la liste des neurones de la couche free(layer->neurons); // On libère enfin la liste des neurones de la couche
} }
free(network->layers);
free(network); // Pour finir, on libère le réseau neuronal contenant la liste des couches free(network); // Pour finir, on libère le réseau neuronal contenant la liste des couches
} }
@ -225,22 +226,22 @@ void network_modification(Network* network, uint32_t nb_modifs) {
else if (neuron->bias < -MAX_RESEAU) else if (neuron->bias < -MAX_RESEAU)
neuron->bias = -MAX_RESEAU; neuron->bias = -MAX_RESEAU;
if (i!=network->nb_layers-1) { if (i != network->nb_layers-1) {
for (int k=0; k < network->layers[i+1]->nb_neurons; k++) { for (int k=0; k < network->layers[i+1]->nb_neurons; k++) {
if (neuron->weights[k] != 0 && PRINT_POIDS) if (neuron->weights[k] != 0 && PRINT_POIDS)
printf("C %d\tN %d -> %d\tp: %f \tDp: %f\n", i, j, k, neuron->weights[k], (LEARNING_RATE/nb_modifs) * neuron->back_weights[k]); printf("C %d\tN %d -> %d\tp: %f \tDp: %f\n", i, j, k, neuron->weights[k], (LEARNING_RATE/nb_modifs) * neuron->back_weights[k]);
neuron->weights[k] -= (LEARNING_RATE/nb_modifs) * neuron->back_weights[k]; // On modifie le poids du neurone à partir des données de la propagation en arrière neuron->weights[k] -= (LEARNING_RATE/nb_modifs) * neuron->back_weights[k]; // On modifie le poids du neurone à partir des données de la propagation en arrière
neuron->back_weights[k] = 0; neuron->back_weights[k] = 0;
if (neuron->weights[k] > MAX_RESEAU) { if (neuron->weights[k] > MAX_RESEAU) {
neuron->weights[k] = MAX_RESEAU; neuron->weights[k] = MAX_RESEAU;
printf("Erreur, max du réseau atteint"); printf("Erreur, max du réseau atteint");
}
else if (neuron->weights[k] < -MAX_RESEAU) {
neuron->weights[k] = -MAX_RESEAU;
printf("Erreur, min du réseau atteint");
}
} }
else if (neuron->weights[k] < -MAX_RESEAU) {
neuron->weights[k] = -MAX_RESEAU;
printf("Erreur, min du réseau atteint");
}
}
} }
} }
} }
@ -286,7 +287,66 @@ void network_initialisation(Network* network) {
} }
} }
void patch_network(Network* network, Network* delta, uint32_t nb_modifs) {
// Les deux réseaux donnés sont supposés de même dimensions
Neuron* neuron;
Neuron* dneuron;
for (int i=0; i < network->nb_layers; i++) {
for (int j=0; j < network->layers[i]->nb_neurons; j++) {
neuron = network->layers[i]->neurons[j];
dneuron = delta->layers[i]->neurons[j];
neuron->bias -= (LEARNING_RATE/nb_modifs) * dneuron->back_bias;
dneuron->back_bias = 0;
if (i != network->nb_layers-1) {
for (int k=0; k < network->layers[i+1]->nb_neurons; k++) {
neuron->weights[k] -= (LEARNING_RATE/nb_modifs) * dneuron->back_weights[k]; // On modifie le poids du neurone à partir des données de la propagation en arrière
dneuron->back_weights[k] = 0;
}
}
}
}
}
Network* copy_network(Network* network) {
// Renvoie une copie modifiable d'un réseau de neurones
Network* network2 = (Network*)malloc(sizeof(Network));
Layer* layer;
Neuron* neuron1;
Neuron* neuron;
network2->nb_layers = network->nb_layers;
network2->layers = (Layer**)malloc(sizeof(Layer*)*network->nb_layers);
for (int i=0; i < network2->nb_layers; i++) {
layer = (Layer*)malloc(sizeof(Layer));
layer->nb_neurons = network->layers[i]->nb_neurons;
layer->neurons = (Neuron**)malloc(sizeof(Neuron*)*layer->nb_neurons);
for (int j=0; j < layer->nb_neurons; j++) {
neuron = (Neuron*)malloc(sizeof(Neuron));
neuron1 = network->layers[i]->neurons[j];
neuron->bias = neuron1->bias;
neuron->z = neuron1->z;
neuron->back_bias = neuron1->back_bias;
neuron->last_back_bias = neuron1->last_back_bias;
if (i != network2->nb_layers-1) {
(void)network2->layers[i+1]->nb_neurons;
neuron->weights = (float*)malloc(sizeof(float)*network->layers[i+1]->nb_neurons);
neuron->back_weights = (float*)malloc(sizeof(float)*network->layers[i+1]->nb_neurons);
neuron->last_back_weights = (float*)malloc(sizeof(float)*network->layers[i+1]->nb_neurons);
for (int k=0; k < network->layers[i+1]->nb_neurons; k++) {
neuron->weights[k] = neuron1->weights[k];
neuron->back_weights[k] = neuron1->back_weights[k];
neuron->last_back_weights[k] = neuron1->last_back_weights[k];
}
}
layer->neurons[j] = neuron;
}
network2->layers[i] = layer;
}
return network2;
}
float loss_computing(Network* network, int numero_voulu){ float loss_computing(Network* network, int numero_voulu){