Add simple_one

src/cnn/creation.c

@@ -47,6 +47,15 @@ Network* create_network_lenet5(float learning_rate, int dropout, int activation,
     return network;
 }
 
+Network* create_simple_one(float learning_rate, int dropout, int activation, int initialisation, int input_dim, int input_depth) {
+    Network* network = create_network(3, learning_rate, dropout, initialisation, input_dim, input_depth);
+    network->kernel[0]->activation = activation;
+    network->kernel[0]->linearisation = 0;
+    add_dense_linearisation(network, 80, activation);
+    add_dense(network, 10, SOFTMAX);
+    return network;
+}
+
 void create_a_cube_input_layer(Network* network, int pos, int depth, int dim) {
     network->input[pos] = (float***)malloc(sizeof(float**)*depth);
     for (int i=0; i < depth; i++) {

src/cnn/include/creation.h

@@ -14,6 +14,11 @@ Network* create_network(int max_size, float learning_rate, int dropout, int init
 */
 Network* create_network_lenet5(float learning_rate, int dropout, int activation, int initialisation, int input_dim, int input_depth);
 
+/*
+* Renvoie un réseau sans convolution, similaire à celui utilisé dans src/mnist
+*/
+Network* create_simple_one(float learning_rate, int dropout, int activation, int initialisation, int input_dim, int input_depth);
+
 /*
 * Créé et alloue de la mémoire à une couche de type input cube
 */

src/cnn/include/train.h

@@ -7,7 +7,7 @@
 #define EPOCHS 10
 #define BATCHES 500
 #define USE_MULTITHREADING
-#define LEARNING_RATE 0.01
+#define LEARNING_RATE 0.5
 
 
 /*

src/cnn/train.c

@@ -50,6 +50,11 @@ void* train_thread(void* parameters) {
             write_image_in_network_32(images[index[i]], height, width, network->input[0][0]);
             forward_propagation(network);
             maxi = indice_max(network->input[network->size-1][0][0], 10);
+            if (maxi == -1) {
+                printf("\n");
+                printf_error("Le réseau sature.\n");
+                exit(1);
+            }
             
             wanted_output = generate_wanted_output(labels[index[i]], 10);
             loss += compute_mean_squared_error(network->input[network->size-1][0][0], wanted_output, 10);
@@ -134,7 +139,8 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
 
     // Initialisation du réseau
     if (!recover) {
-        network = create_network_lenet5(LEARNING_RATE, 0, TANH, GLOROT, input_dim, input_depth);
+        network = create_network_lenet5(LEARNING_RATE, 0, RELU, GLOROT, input_dim, input_depth);
+        //network = create_simple_one(LEARNING_RATE, 0, RELU, GLOROT, input_dim, input_depth);
     } else {
         network = read_network(recover);
         network->learning_rate = LEARNING_RATE;
@@ -272,7 +278,7 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
                     }
                 }
                 current_accuracy = accuracy * nb_images_total/((j+1)*BATCHES);
-                printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "YELLOW"%0.2f%%"RESET" ", nb_threads, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
+                printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "YELLOW"%0.2f%%"RESET" \tRéussies: %d", nb_threads, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100, (int)(accuracy*nb_images_total));
                 fflush(stdout);
             #else
                 (void)nb_images_total_remaining; // Juste pour enlever un warning
@@ -294,19 +300,19 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
                 update_weights(network, network);
                 update_bias(network, network);
 
-                printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "YELLOW"%0.4f%%"RESET" ", i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
+                printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "YELLOW"%0.4f%%"RESET" \tRéussies: %d", i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100, (int)(accuracy*nb_images_total));
                 fflush(stdout);
             #endif
             // Il serait intéressant d'utiliser la perte calculée pour
             // savoir l'avancement dans l'apprentissage et donc comment adapter le taux d'apprentissage
-            //network->learning_rate = 0.01*batch_loss;
+            network->learning_rate = 10*LEARNING_RATE*batch_loss;
         }
         end_time = omp_get_wtime();
         elapsed_time = end_time - start_time;
         #ifdef USE_MULTITHREADING
-        printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "GREEN"%0.4f%%"RESET"\tTemps: %0.2f s\n", nb_threads, i, epochs, nb_images_total, nb_images_total, accuracy*100, elapsed_time);
+        printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "GREEN"%0.4f%%"RESET" \tRéussies: %d\tTemps: %0.2f s\n", nb_threads, i, epochs, nb_images_total, nb_images_total, accuracy*100, (int)(accuracy*nb_images_total), elapsed_time);
         #else
-        printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "GREEN"%0.4f%%"RESET"\tTemps: %0.2f s\n", i, epochs, nb_images_total, nb_images_total, accuracy*100, elapsed_time);
+        printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "GREEN"%0.4f%%"RESET" \tRéussies: %d\tTemps: %0.2f s\n", i, epochs, nb_images_total, nb_images_total, accuracy*100, (int)(accuracy*nb_images_total), elapsed_time);
         #endif
         write_network(out, network);
     }