From b1104d9fb4eae26568041f577aee8d0c08cdb656 Mon Sep 17 00:00:00 2001
From: mirimatcode <mirimatcode@gmail.com>
Date: Fri, 14 Feb 2025 16:55:56 +0100
Subject: [PATCH] niente, non converge nemmeno con mnist

---
 classificatore_singolo   | Bin 21184 -> 21352 bytes
 classificatore_singolo.c |  53 ++++++++++++++++++---------------------
 mnist/mnist_manager      | Bin 15952 -> 15952 bytes
 mnist/mnist_manager.h    |   7 +++++-
 percettroni.h            |  22 ++++++++++------
 rete_neurale             | Bin 20640 -> 0 bytes
 rete_pesi.bin            | Bin 0 -> 891876 bytes
 visualizzatore           | Bin 22320 -> 22448 bytes
 visualizzatore.c         |   6 ++---
 9 files changed, 48 insertions(+), 40 deletions(-)
 delete mode 100755 rete_neurale
 create mode 100644 rete_pesi.bin

diff --git a/classificatore_singolo b/classificatore_singolo
index 820ba44c94e541dd444a5d0b63c07a501752e5e2..6c66ed33215cea39edf41846f98df2109055214d 100755
GIT binary patch
delta 4724
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diff --git a/classificatore_singolo.c b/classificatore_singolo.c
index 6308a5b..4c0361c 100644
--- a/classificatore_singolo.c
+++ b/classificatore_singolo.c
@@ -19,19 +19,21 @@
 #define PERCETTRONI_LAYER_4 1
 #define INPUT_LAYER_4 PERCETTRONI_LAYER_3
 
-#define MAX_EPOCHE 1000
+#define MAX_EPOCHE 10
 
 //Scelgo quale categoria voglio identificare. La 7 sono i cavalli. La rete mi dirà per ogni immagine se è un cavallo o no
 #define CATEGORIA 7
+
 byte get_out_corretto(byte);
 void stampa_layer_indirizzo(Layer*);
+void stampa_tempo(time_t[], int);
 
 void main() {
     time_t tempo_epoche[MAX_EPOCHE];
 
     srand(time(NULL));
 
-    Dataset *set_appoggio = get_dataset("mnist/t10k-images.idx3-ubyte", "mnist/t10k-labels.idx1-ubyte");
+    Dataset *set_appoggio = get_dataset(file_immagini, file_label);
 
     if(set_appoggio == NULL)
         return;
@@ -39,7 +41,7 @@ void main() {
     free(set_appoggio);
 
     ReteNeurale rete_neurale;
-    ReteNeurale *puntatore_rete = caricaReteNeurale("rete_cifar_pesi.bin");
+    ReteNeurale *puntatore_rete = caricaReteNeurale(file_pesi);
     if(puntatore_rete == NULL) {
         rete_neurale = inizializza_rete_neurale(NUM_LAYERS);
         //inizializzo layer 0
@@ -51,7 +53,7 @@ void main() {
         //inizializzo layer 3
         rete_neurale.layers[3] = inizializza_layer(PERCETTRONI_LAYER_3, INPUT_LAYER_3);
         //inizializzo layer ULTIMO
-        //rete_neurale.layers[4] = inizializza_layer(PERCETTRONI_LAYER_4, INPUT_LAYER_4);
+        rete_neurale.layers[4] = inizializza_layer(PERCETTRONI_LAYER_4, INPUT_LAYER_4);
     } else {
         rete_neurale = *puntatore_rete;
         free(puntatore_rete);
@@ -62,26 +64,12 @@ void main() {
 
     //ADDESTRAMENTO
     for(int i = 0; i < MAX_EPOCHE; i++) {
-        time(&tempo_epoche[i]);
         
-        if(i == 0)
-            printf("Epoca %d\n", i);
-        else {
-            time(&tempo_epoche[i]);
-            double tempo_trascorso_epoca = difftime(tempo_epoche[i], tempo_epoche[i-1]);
-            double tempo_trascorso_totale = difftime(tempo_epoche[i], tempo_epoche[0]);
-            int minuti_epoca = (int)tempo_trascorso_epoca / 60;
-            int secondi_epoca = (int)tempo_trascorso_epoca % 60;
-            int minuti_totali = (int)tempo_trascorso_totale / 60;
-            int secondi_totali = (int)tempo_trascorso_totale % 60;
-            printf("Epoca %d\n", i);
-            printf("Tempo dall'epoca precedente: %d:%d\n", minuti_epoca, secondi_epoca);
-            printf("Tempo dall'inizio: %d:%d\n", minuti_totali, secondi_totali);
-        }
-
+        printf("Epoca %d\n", i);
+        stampa_tempo(tempo_epoche, i);
         int corrette = 0;
 
-        for(int indice_set = 0; indice_set < set.size -1; indice_set++) {
+        for(int indice_set = 0; indice_set < set.size; indice_set++) {
 
             double **sigmoidi = (double **)malloc(sizeof(double*) * NUM_LAYERS);
 
@@ -93,14 +81,10 @@ void main() {
                 sigmoidi[j] = funzioni_attivazione_layer_double(rete_neurale.layers[j], sigmoidi[j-1]);
             }
 
-            //printf("\timmagine: %d post sigmoidi\n", indice_set);
-
             byte output_corretto = get_out_corretto(set.istanze[indice_set].categoria);
 
-            
             //Se prevede male
             if(previsione(sigmoidi[NUM_LAYERS-1][0]) != output_corretto) {
-
                 double **gradienti = (double**)malloc(sizeof(double*) * NUM_LAYERS);
 
                 for(int indice_layer = 0; indice_layer < NUM_LAYERS; indice_layer++) {
@@ -116,14 +100,12 @@ void main() {
             {
                 corrette++;
             }
-
-            //printf("\timmagine: %d post correzioni\n", indice_set);
         }
         
         printf("\tRisposte corrette: %d\n", corrette);
     }
 
-    salvaReteNeurale("rete_cifar_pesi.bin", &rete_neurale);
+    salvaReteNeurale(file_pesi, &rete_neurale);
 }
 
 //Questa funzione ritorna 1 se la categoria è quella che voglio individuare, altrimenti 0
@@ -143,4 +125,19 @@ void stampa_layer_indirizzo(Layer *layer) {
         }
         printf("\n");
     }
+}
+
+void stampa_tempo(time_t tempo_epoche[], int i) {
+
+    time(&tempo_epoche[i]);
+    if(i > 0) {
+        double tempo_trascorso_epoca = difftime(tempo_epoche[i], tempo_epoche[i-1]);
+        double tempo_trascorso_totale = difftime(tempo_epoche[i], tempo_epoche[0]);
+        int minuti_epoca = (int)tempo_trascorso_epoca / 60;
+        int secondi_epoca = (int)tempo_trascorso_epoca % 60;
+        int minuti_totali = (int)tempo_trascorso_totale / 60;
+        int secondi_totali = (int)tempo_trascorso_totale % 60;
+        printf("Tempo dall'epoca precedente: %d:%d\n", minuti_epoca, secondi_epoca);
+        printf("Tempo dall'inizio: %d:%d\n", minuti_totali, secondi_totali);
+    }
 }
\ No newline at end of file
diff --git a/mnist/mnist_manager b/mnist/mnist_manager
index 6ca176f17fba6efff1766a86a8e96b691e3d29f8..269d8bc9c40622b02bf540244acb0dc4d8d33d42 100755
GIT binary patch
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diff --git a/mnist/mnist_manager.h b/mnist/mnist_manager.h
index 74eec4d..b967796 100644
--- a/mnist/mnist_manager.h
+++ b/mnist/mnist_manager.h
@@ -9,6 +9,11 @@ Byte 16 in poi: Dati delle immagini (ogni immagine è composta da 28x28 = 784 by
 Le immagini sono 28x28 pixel.
 I dati sono byte non firmati (valori da 0 a 255).
 Il file contiene 60.000 immagini (per il training set),
+
+Label
+Byte 0-3: Numero magico (0x00000801).
+Byte 4-7: Numero di label (60.000).
+Byte 8 in poi: 60.000 byte, ognuno dei quali rappresenta l'etichetta di un'immagine.
 */
 
 #include <stdlib.h>
@@ -59,7 +64,7 @@ Dataset *get_dataset(char *path_mnist, char *path_categoria)
     int numero_righe = 0;
 
     //Leggo male il file, cambiare in base alle dichiarazioni sopra
-    if(fread(istanze[numero_righe].immagine, sizeof(byte), 16, file) == 16)
+    if(fread(istanze[numero_righe].immagine, sizeof(byte), 16, file) == 16 && fread(&istanze[numero_righe].categoria, sizeof(byte), 8, categorie) == 8)
         while (fread(istanze[numero_righe].immagine, sizeof(byte), N_PIXEL, file) == N_PIXEL)
         {
             if(fread(&istanze[numero_righe].categoria, sizeof(byte), 1, categorie) == 1) {
diff --git a/percettroni.h b/percettroni.h
index 0c5caf1..d7c3a14 100644
--- a/percettroni.h
+++ b/percettroni.h
@@ -2,11 +2,15 @@
 #include <stdlib.h>
 #include <math.h>
 
+char *file_pesi = "rete_pesi.bin";
+char *file_immagini = "mnist/t10k-images.idx3-ubyte";
+char *file_label = "mnist/t10k-labels.idx1-ubyte";
+
 // Siccome il char è un byte che rappresenta il valore tra 0 e 255. Per evitare confusioni definisco il tipo "byte" come in Java
 typedef unsigned char byte;
 
 
-double LRE = 0.1;
+double LRE = 0.2;
 double soglia_sigmoide = 0.5;
 
 typedef struct {
@@ -47,7 +51,7 @@ ReteNeurale *caricaReteNeurale(const char*);
 //Questa funzione genera un valore reale random compreso nell'intervallo [-1, 1]
 double randomico() {
     // Genero numeri nell'intervallo [-1,1]
-    return ((double)(rand() % 101 * 0.01 * 2 ) -1);
+    return (double)((rand() / RAND_MAX) * -1);//((double)(rand() % 101 * 0.01 * 2 ) -1);
 }
 
 //Questa funzione inizializza il percettrone allocando la memoria in base al numero dei pesi che voglio ed inizializza il loro valore usando randomico()
@@ -130,6 +134,7 @@ double *funzioni_attivazione_layer_byte(Layer layer, byte *inputs) {
     
     for(int i = 0; i < layer.size; i++) {
         funzioni[i] = sigmoide_byte(layer.percettroni[i], inputs, layer.percettroni[i].size);
+        //printf("\tsigmoide layer input %f\n", funzioni[i]);
     }
 
     return funzioni;
@@ -142,6 +147,7 @@ double *funzioni_attivazione_layer_double(Layer layer, double *inputs) {
     
     for(int i = 0; i < layer.size; i++) {
         funzioni[i] = sigmoide_double(layer.percettroni[i], inputs, layer.percettroni[i].size);
+        //printf("\tsigmoide layer %d: %f\n", i, funzioni[i]);
     }
 
     return funzioni;
@@ -162,9 +168,9 @@ void correggi_layer_interni(ReteNeurale *rete, double **gradienti, double **sigm
         for(int indice_percettrone = 0; indice_percettrone < rete->layers[indice_layer].size; indice_percettrone++) {//Numero percettroni
 
             for(int indice_peso = 0; indice_peso < rete->layers[indice_layer].percettroni[indice_percettrone].size; indice_peso++) {//Numero pesi
-                gradienti[indice_layer][indice_percettrone] = gradienti[rete->size-1][0] * (sigmoidi[indice_layer][indice_percettrone] * (1 - sigmoidi[indice_layer][indice_percettrone]));
-                rete->layers[indice_layer].percettroni[indice_percettrone].pesi[indice_peso] += (gradienti[indice_layer][indice_percettrone] * LRE * sigmoidi[indice_layer-1][indice_percettrone]);           
-                //rete->layers[indice_layer].percettroni[indice_percettrone].pesi[indice_peso] += (gradienti[rete->size-1][0] * LRE * sigmoidi[indice_layer-1][indice_percettrone]);
+                //gradienti[indice_layer][indice_percettrone] = gradienti[rete->size-1][0] * (sigmoidi[indice_layer][indice_percettrone] * (1 - sigmoidi[indice_layer][indice_percettrone]));
+                //rete->layers[indice_layer].percettroni[indice_percettrone].pesi[indice_peso] += (gradienti[indice_layer][indice_percettrone] * LRE * sigmoidi[indice_layer-1][indice_percettrone]);           
+                rete->layers[indice_layer].percettroni[indice_percettrone].pesi[indice_peso] += (gradienti[rete->size-1][0] * LRE * sigmoidi[indice_layer-1][indice_percettrone]);
             }
             rete->layers[indice_layer].percettroni[indice_percettrone].bias += (gradienti[indice_layer][indice_percettrone] * LRE);
             //printf("bias: %f\n", rete->layers[indice_layer].percettroni[indice_percettrone].bias);
@@ -179,9 +185,9 @@ void correggi_layer_input(Layer *layer, double **gradienti, double **sigmoidi, b
     for(int indice_percettrone = 0; indice_percettrone < layer->size; indice_percettrone++) {//Numero percettroni
         for(int indice_peso = 0; indice_peso < layer->percettroni->size; indice_peso++) { //Numero pesi
 
-            gradienti[indice_layer][indice_percettrone] = gradienti[n_layers-1][0] * (sigmoidi[indice_layer][indice_percettrone] * (1 - sigmoidi[indice_layer][indice_percettrone]));
-            layer->percettroni[indice_percettrone].pesi[indice_peso] += (gradienti[indice_layer][indice_percettrone] * LRE * inputs[indice_peso]);
-            //layer->percettroni[indice_percettrone].pesi[indice_peso] += (gradienti[n_layers-1][0] * LRE * inputs[indice_peso]);
+            //gradienti[indice_layer][indice_percettrone] = gradienti[n_layers-1][0] * (sigmoidi[indice_layer][indice_percettrone] * (1 - sigmoidi[indice_layer][indice_percettrone]));
+            //layer->percettroni[indice_percettrone].pesi[indice_peso] += (gradienti[indice_layer][indice_percettrone] * LRE * inputs[indice_peso]);
+            layer->percettroni[indice_percettrone].pesi[indice_peso] += (gradienti[n_layers-1][0] * LRE * inputs[indice_peso]);
         }
         layer->percettroni[indice_percettrone].bias += (gradienti[n_layers-1][0] * LRE);
     }
diff --git a/rete_neurale b/rete_neurale
deleted file mode 100755
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literal 0
HcmV?d00001

diff --git a/visualizzatore b/visualizzatore
index 5e493b0ce4decf1a9210342d9cc21e266046bc93..cf86a31fbe13b88a2c5a5897ae12250913b06288 100755
GIT binary patch
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Ang9R*

diff --git a/visualizzatore.c b/visualizzatore.c
index e9e1ce2..c35890d 100644
--- a/visualizzatore.c
+++ b/visualizzatore.c
@@ -33,13 +33,13 @@ void main()
     init_allegro();
 
     //get_dataset("cifar-10-batches/test_batch.bin");
-    set = get_dataset("mnist/t10k-images.idx3-ubyte", "mnist/t10k-labels.idx1-ubyte");
+    set = get_dataset(file_immagini, file_label);
     if (set == NULL) {
         printf("Errore nel caricare il dataset\n");
         return;
     }
 
-    rete_neurale = caricaReteNeurale("rete_cifar_pesi.bin");
+    rete_neurale = caricaReteNeurale(file_pesi);
     if (rete_neurale == NULL) {
         printf("Errore nel caricare il modello\n");
         return;
@@ -94,7 +94,7 @@ void init_allegro() {
 void carica_immagine(int indice_set)
 {
     // Stampa informazioni sull'immagine
-    printf("Immagine indice: %d, categoria: %d\n", indice_set, set->istanze[indice_set].categoria);
+    printf("Immagine indice: %d, valore: %d. è un 7? %d\n", indice_set, set->istanze[indice_set].categoria, prevedi(indice_set));
 
     // Itera su ogni pixel dell'immagine
     for (int y = 0; y < IMAGE_HEIGHT; y++)