From daa34cb8bc1687e2d62231b8623ef29fe0ca7508 Mon Sep 17 00:00:00 2001
From: mirimatcode <mirimatcode@gmail.com>
Date: Sat, 22 Feb 2025 18:48:02 +0100
Subject: [PATCH] ottimizzato il codice, messo relu(ma non attivato), pronto
 per prossima sessione di test

---
 classificatore_singolo      | Bin 21344 -> 21504 bytes
 classificatore_singolo.c    |  53 ++++++++-----------
 classificatore_singolo_xor  | Bin 21344 -> 0 bytes
 mnist/mnist_manager         | Bin 15952 -> 0 bytes
 mnist/mnist_manager.h       |  12 +++++
 percettroni.h               |  98 ++++++++++++++++++++++++------------
 prova_passaggio_parametri.c |  40 +++++++++++++++
 visualizzatore              | Bin 22448 -> 26752 bytes
 visualizzatore.c            |  55 +++++++++++---------
 9 files changed, 170 insertions(+), 88 deletions(-)
 delete mode 100755 classificatore_singolo_xor
 delete mode 100755 mnist/mnist_manager
 create mode 100644 prova_passaggio_parametri.c

diff --git a/classificatore_singolo b/classificatore_singolo
index 5b9c29a760678bacfd701652cabf82515dde2878..17c7706ed4224480030f1234fd0865b9eaaebaf2 100755
GIT binary patch
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diff --git a/classificatore_singolo.c b/classificatore_singolo.c
index da47838..0ba977c 100644
--- a/classificatore_singolo.c
+++ b/classificatore_singolo.c
@@ -5,7 +5,10 @@
 #define CATEGORIA 7
 #define NUM_LAYERS 4
 #define PERCETTRONI_LAYER_0 64
-#define MAX_EPOCHE 1000
+#define MAX_EPOCHE 10
+
+//1 relu, 2 sigmoide
+#define TIPO_FUNZIONE 2
 
 byte get_out_corretto(byte);
 void stampa_layer_indirizzo(Layer *);
@@ -45,34 +48,19 @@ void main()
     // ADDESTRAMENTO
     for (int i = 0; i < MAX_EPOCHE; i++)
     {
-        /* if (corrette == 4)
-        {
-            printf("\nConvergo in epoche: %d\n", i);
-
-            // stampa_risultati_layer_multi(p_ext_1, p_ext_2, pout);
-            for (int j = 0; j < 4; j++)
-            {
-                double **risultato = elabora_sigmoidi(rete_neurale, set.istanze[j]);
-
-                printf("Input: [%d,%d] -> probabilità: %f -> risultato atteso: %d\n", set.istanze[j].dati[0], set.istanze[j].dati[1], risultato[NUM_LAYERS - 1][0], set.istanze[j].classificazione);
-            }
-
-            break;
-        } */
-
-        // printf("Epoca %d\n", i);
-        // stampa_tempo(tempo_epoche, i);
+        printf("Epoca %d\n", i);
+        stampa_tempo(tempo_epoche, i);
         corrette = 0;
         double errore_totale = 0.0;
         
         for (int indice_set = 0; indice_set < set.size -1; indice_set++)
         {
             //printf("Qui ci arrivo %d\n", indice_set);
-            double **sigmoidi = elabora_sigmoidi(rete_neurale, set.istanze[indice_set]);
+            double **funzioni_attivazione = elabora_funzioni_attivazione(rete_neurale, set.istanze[indice_set], TIPO_FUNZIONE);
 
             /* for(int k = 0; k < rete_neurale.size; k++)
                 for(int j = 0; j < rete_neurale.layers[k].size; j++)
-                    printf("sigmoide[%d][%d] = %f\n", k, j, sigmoidi[k][j]); */
+                    printf("sigmoide[%d][%d] = %f\n", k, j, funzioni_attivazione[k][j]); */
 
             byte output_corretto = get_out_corretto(set.istanze[indice_set].classificazione);
 
@@ -87,19 +75,19 @@ void main()
             
 
             // Derivata funzione di perdita
-            // printf("output_corretto = %d, previsione: %f\n", output_corretto, sigmoidi[NUM_LAYERS - 1][0]);
-            double gradiente_errore = (output_corretto - sigmoidi[NUM_LAYERS - 1][0]);
+            // printf("output_corretto = %d, previsione: %f\n", output_corretto, funzioni_attivazione[NUM_LAYERS - 1][0]);
+            double gradiente_errore = (output_corretto - funzioni_attivazione[NUM_LAYERS - 1][0]);
 
-            errore_totale += pow(gradiente_errore, 2);
+            errore_totale += pow(gradiente_errore, 2) * 0.5;
 
             // Derivata funzione attivazione
-            double derivata_sigmoide_out = sigmoidi[NUM_LAYERS - 1][0] * (1.0 - sigmoidi[NUM_LAYERS - 1][0]);
-            // if (derivata_sigmoide_out == 0.0) derivata_sigmoide_out = 1;
+            double derivata_funzione_out = derivata_sigmoide(funzioni_attivazione[NUM_LAYERS - 1][0]);
+            // if (derivata_funzione_out == 0.0) derivata_funzione_out = 1;
 
             // Gradiente del percettrone output
-            gradienti[NUM_LAYERS - 1][0] = gradiente_errore * derivata_sigmoide_out;
+            gradienti[NUM_LAYERS - 1][0] = gradiente_errore * derivata_funzione_out;
 
-            discesa_gradiente(rete_neurale, sigmoidi, gradienti);
+            discesa_gradiente(rete_neurale, funzioni_attivazione, gradienti, TIPO_FUNZIONE);
 
             // A questo punto ho tutti i gradienti dei percettroni, non mi resta che trovare i gradienti dei pesi e correggerli
 
@@ -107,7 +95,7 @@ void main()
             for (int indice_peso = 0; indice_peso < rete_neurale.layers[NUM_LAYERS - 1].percettroni[0].size; indice_peso++)
             {
                 // Determino gradiente del peso
-                double gradiente_peso = gradienti[NUM_LAYERS - 1][0] * sigmoidi[NUM_LAYERS - 2][indice_peso];
+                double gradiente_peso = gradienti[NUM_LAYERS - 1][0] * funzioni_attivazione[NUM_LAYERS - 2][indice_peso];
                 rete_neurale.layers[NUM_LAYERS - 1].percettroni[0].pesi[indice_peso] += gradiente_peso * LRE;
             }
             rete_neurale.layers[NUM_LAYERS - 1].percettroni[0].bias += gradienti[NUM_LAYERS - 1][0] * LRE;
@@ -121,7 +109,7 @@ void main()
                     // Devo prendere il gradiente del percettrone e moltiplicarlo con gli input associati ai pesi
                     if (indice_layer != 0)
                     {
-                        correggi_pesi_percettrone_double(&rete_neurale.layers[indice_layer].percettroni[indice_percettrone], indice_layer, sigmoidi, gradienti[indice_layer][indice_percettrone]);
+                        correggi_pesi_percettrone_double(&rete_neurale.layers[indice_layer].percettroni[indice_percettrone], indice_layer, funzioni_attivazione, gradienti[indice_layer][indice_percettrone]);
                     }
                     else
                     {
@@ -130,13 +118,14 @@ void main()
                 }
             }
 
-            if (previsione(sigmoidi[NUM_LAYERS - 1][0]) == output_corretto)
+            if (previsione(funzioni_attivazione[NUM_LAYERS - 1][0]) == output_corretto)
             {
                 corrette++;
             }
         }
 
-        printf("Errore: %f, risposte corrette: %d/%d\n", errore_totale / 10000, corrette, set.size);
+        errore_totale /= 10000;
+        printf("Errore: %f, risposte corrette: %d/%d\n", errore_totale, corrette, set.size);
         // printf("\tRisposte corrette: %d\n", corrette);
 
         /* if (i == MAX_EPOCHE - 1)
@@ -145,7 +134,7 @@ void main()
             double **risultato;
             for (int j = 0; j < 4; j++)
             {
-                risultato = elabora_sigmoidi(rete_neurale, set.istanze[j]);
+                risultato = elabora_funzioni_attivazione(rete_neurale, set.istanze[j]);
                 printf("Input: [%d,%d] -> probabilità: %f -> previsione: %d -> risultato attesto: %d\n", set.istanze[j].dati[0], set.istanze[j].dati[1], risultato[NUM_LAYERS - 1][0], previsione(risultato[NUM_LAYERS - 1][0]), set.istanze[j].classificazione);
                 for (int k = 0; k < rete_neurale.size; k++)
                     for (int j = 0; j < rete_neurale.layers[k].size; j++)
diff --git a/classificatore_singolo_xor b/classificatore_singolo_xor
deleted file mode 100755
index d0b75e9cd3cff022791fc875bc19e73a4af62567..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/mnist/mnist_manager b/mnist/mnist_manager
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diff --git a/mnist/mnist_manager.h b/mnist/mnist_manager.h
index d2c0a7a..0d9516e 100644
--- a/mnist/mnist_manager.h
+++ b/mnist/mnist_manager.h
@@ -78,6 +78,18 @@ Dataset *get_dataset(char *path_mnist, char *path_categoria)
     (*set).istanze = istanze;
 
     fclose(file);
+
+    //Trasformo tutto in 0 e 1
+
+    /* for(int indice_immagine = 0; indice_immagine < set->size; indice_immagine++) {
+        for(int indice_byte = 0; indice_byte < N_INPUTS; indice_byte++) {
+            if(set->istanze[indice_immagine].dati[indice_byte] >= 128)
+                set->istanze[indice_immagine].dati[indice_byte] = 1;
+            else
+                set->istanze[indice_immagine].dati[indice_byte] = 0;
+        }
+    } */
+
     return set;
 }
 
diff --git a/percettroni.h b/percettroni.h
index b3fac51..e5e9350 100644
--- a/percettroni.h
+++ b/percettroni.h
@@ -3,8 +3,12 @@
 #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";
+char *file_immagini = "mnist/train-images.idx3-ubyte";
+char *file_label = "mnist/train-labels.idx1-ubyte";
+
+//Test dataset
+/* char *file_immagini = "mnist/t10k-images.idx3-ubyte";
+char *file_label = "mnist/t10k-labels.idx1-ubyte"; */
 
 #include "mnist/mnist_manager.h"
 // #include "cifar_10/cifar10_manager.h"
@@ -13,7 +17,7 @@ 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 = 2;
+double LRE = 1.414;
 double soglia_sigmoide = 0.5;
 
 typedef struct
@@ -41,14 +45,18 @@ Percettrone inzializza_percettrone(int);
 ReteNeurale inizializza_rete_neurale(int, int, int);
 Layer inizializza_layer(int, int);
 
-double sigmoide(Percettrone p, double*);
-double **elabora_sigmoidi(ReteNeurale, Istanza);
-void discesa_gradiente(ReteNeurale, double **, double **);
+double funzione_attivazione(Percettrone p, double*, int);
+double **elabora_funzioni_attivazione(ReteNeurale, Istanza, int);
+void discesa_gradiente(ReteNeurale, double **, double **, int);
 double calcola_gradiente_disceso(ReteNeurale, int, int, double **);
 void correggi_pesi_percettrone_double(Percettrone *, int, double **, double);
 void correggi_pesi_percettrone_byte(Percettrone *, Istanza, double, int);
 
 int previsione(double);
+double sigmoide(double);
+double derivata_sigmoide(double);
+double relu(double);
+double derivata_relu(double);
 
 void salvaReteNeurale(const char *, ReteNeurale *);
 ReteNeurale *caricaReteNeurale(const char *);
@@ -126,8 +134,8 @@ ReteNeurale inizializza_rete_neurale(int numero_layers, int numero_percettroni_i
     ################# PREVISIONI ################################
 */
 
-
-double sigmoide(Percettrone p, double *valori)
+//Tipo 1 = relu, tipo 2 = sigmoide
+double funzione_attivazione(Percettrone p, double *valori, int tipo)
 {
     double sommatoria = 0.0;
     for (int i = 0; i < p.size; i++) {
@@ -136,10 +144,20 @@ double sigmoide(Percettrone p, double *valori)
     }
 
     sommatoria += p.bias;
-    double risultato = 1.0 / (1.0 + exp(-sommatoria));
     //printf(" sommatoria %f -> %f\n",sommatoria, risultato);
 
-    return risultato;
+    if(tipo == 1)
+        return relu(sommatoria);
+    
+    return sigmoide(sommatoria);
+}
+
+double sigmoide(double valore) {
+    return 1.0 / (1.0 + exp(-valore));
+}
+
+double derivata_sigmoide(double valore) {
+    return valore * (1.0 - valore);
 }
 
 int previsione(double valore)
@@ -149,31 +167,42 @@ int previsione(double valore)
     else
         return 0;
 }
- 
-void discesa_gradiente(ReteNeurale rete, double **sigmoidi, double **gradienti)
+
+double relu(double valore) {
+    if(valore > 0)
+        return valore;
+    
+    return 0;
+}
+
+double derivata_relu(double valore) {
+    if(valore > 0)
+        return 1;
+
+    return 0;
+}
+
+void discesa_gradiente(ReteNeurale rete, double **funzioni, double **gradienti, int tipo_derivata)
 {
-    //printf("Qui?\n");
-    // For che scorre i layer dal penultimo al primo QUINI SIZE -2
     for (int indice_layer = rete.size - 2; indice_layer >= 0; indice_layer--)
     {
-        //printf("layer: %d ", indice_layer);
-        // printf("Mi trovo nel layer %d, ho %d percettroni\n", indice_layer, rete.layers[indice_layer].size);
-
-        // For che scorre i percettroni del layer partendo dal primo
-        // Per ogni percettrone mi devo prendere il gradiente disceso dal livello sopra e moltiplicarlo per la derivata di attivazione
         for (int indice_percettrone = 0; indice_percettrone < rete.layers[indice_layer].size; indice_percettrone++)
         {
-            //printf("percettrone: %d ", indice_percettrone);
-            double derivata_attivazione = sigmoidi[indice_layer][indice_percettrone] * (1.0 - sigmoidi[indice_layer][indice_percettrone]);
+            //In base al tipo di funzione scelto, avvio relu o sigmoidi
+
+            double derivata_attivazione;
+
+            if(tipo_derivata == 1)
+                derivata_attivazione = derivata_relu(funzioni[indice_layer][indice_percettrone]);
+            else
+                derivata_attivazione = derivata_sigmoide(funzioni[indice_layer][indice_percettrone]);
 
             // Passo anche l'indice del percettrone perchè corrisponde all'indice del peso del livello sopra
             double gradiente_disceso = calcola_gradiente_disceso(rete, indice_layer + 1, indice_percettrone, gradienti);
 
             gradienti[indice_layer][indice_percettrone] = gradiente_disceso * derivata_attivazione;
         }
-        //printf("\n");
     }
-    //printf("Qui ?\n");
 }
 
 double calcola_gradiente_disceso(ReteNeurale rete, int livello, int indice_peso, double **gradienti)
@@ -190,29 +219,34 @@ double calcola_gradiente_disceso(ReteNeurale rete, int livello, int indice_peso,
     return sommatoria;
 }
 
-double **elabora_sigmoidi(ReteNeurale rete, Istanza istanza)
+//tipo_funzione 1: relu, 2:sigmoide
+double **elabora_funzioni_attivazione(ReteNeurale rete, Istanza istanza, int tipo_funzione)
 {
-    // sigmoidi è un array bidimensionale, la prima dimensione identifica il layer, la seconda il percettrone nel layer
-    // sigmoidi[indice_layer][indice_percettrone]
-    double **sigmoidi = (double **)malloc(sizeof(double *) * rete.size);
+
+    double **funzioni = (double **)malloc(sizeof(double *) * rete.size);
     double *inputs = (double *)malloc(sizeof(double *) * N_INPUTS);
     for(int i = 0; i < N_INPUTS; i++) {
         inputs[i] = (double)istanza.dati[i];
     }
 
-    sigmoidi[0] = (double *)malloc(sizeof(double) * rete.layers[0].size);
+    funzioni[0] = (double *)malloc(sizeof(double) * rete.layers[0].size);
     for(int indice_percettrone = 0; indice_percettrone < rete.layers[0].size; indice_percettrone ++) {
-        sigmoidi[0][indice_percettrone] = sigmoide(rete.layers[0].percettroni[indice_percettrone], inputs);
+        funzioni[0][indice_percettrone] = funzione_attivazione(rete.layers[0].percettroni[indice_percettrone], inputs, tipo_funzione);
     }
 
+    //Taglio fuori l'ultimo layer perchè in ogni caso sarà sigmoide
     for(int indice_layer = 1; indice_layer < rete.size; indice_layer ++) {
-        sigmoidi[indice_layer] = (double *)malloc(sizeof(double) * rete.layers[indice_layer].size);
+        funzioni[indice_layer] = (double *)malloc(sizeof(double) * rete.layers[indice_layer].size);
         for(int indice_percettrone = 0; indice_percettrone < rete.layers[indice_layer].size; indice_percettrone ++) {
-            sigmoidi[indice_layer][indice_percettrone] = sigmoide(rete.layers[indice_layer].percettroni[indice_percettrone], sigmoidi[indice_layer-1]);
+            //Se è il livello output in ogni caso deve fare sigmoide
+            if(indice_layer == rete.size-1)
+                funzioni[indice_layer][indice_percettrone] = funzione_attivazione(rete.layers[indice_layer].percettroni[indice_percettrone], funzioni[indice_layer-1], 2);
+            else
+                funzioni[indice_layer][indice_percettrone] = funzione_attivazione(rete.layers[indice_layer].percettroni[indice_percettrone], funzioni[indice_layer-1], tipo_funzione);
         }
     }
 
-    return sigmoidi;
+    return funzioni;
 }
 
 
diff --git a/prova_passaggio_parametri.c b/prova_passaggio_parametri.c
new file mode 100644
index 0000000..7128446
--- /dev/null
+++ b/prova_passaggio_parametri.c
@@ -0,0 +1,40 @@
+#include<stdio.h>
+#include<stdlib.h>
+
+void stampa(int**);
+void cambia(int**);
+
+void main() {
+    int **matrice = (int**)malloc(sizeof(int*) * 10);
+
+    for(int i = 0; i < 10; i++) {
+        matrice[i] = (int*)malloc(sizeof(int) * 10);
+        for(int j = 0; j < 10; j++) {
+            matrice[i][j] = i * j;
+        }
+    }
+
+    stampa(matrice);
+
+    cambia(matrice);
+
+    stampa(matrice);
+}
+
+void stampa(int **matrix) {
+    for(int i = 0; i < 10; i++) {
+        
+        for(int j = 0; j < 10; j++) {
+            printf(" %d ", matrix[i][j]);
+        }
+        printf("\n");
+    }
+}
+
+void cambia(int **matrix) {
+    for(int i = 0; i < 10; i++) {
+        for(int j = 0; j < 10; j++) {
+            matrix[i][j] = 0;
+        }
+    }
+}
\ No newline at end of file
diff --git a/visualizzatore b/visualizzatore
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diff --git a/visualizzatore.c b/visualizzatore.c
index c35890d..d501fd5 100644
--- a/visualizzatore.c
+++ b/visualizzatore.c
@@ -1,7 +1,6 @@
 #include <allegro.h>
 #include <time.h>
 #include "percettroni.h"
-#include "mnist/mnist_manager.h"
 
 //CIFAR_10
 /* #define IMAGE_WIDTH 32
@@ -17,8 +16,8 @@
 BITMAP *buffer;
 BITMAP *image;
 int *previsto;
-ReteNeurale *rete_neurale;
-Dataset *set;
+ReteNeurale rete_neurale;
+Dataset set;
 
 
 void init_allegro();
@@ -33,26 +32,42 @@ void main()
     init_allegro();
 
     //get_dataset("cifar-10-batches/test_batch.bin");
-    set = get_dataset(file_immagini, file_label);
-    if (set == NULL) {
+    Dataset *set_appoggio = get_dataset(file_immagini, file_label);
+    if (set_appoggio == NULL) {
         printf("Errore nel caricare il dataset\n");
         return;
     }
+    set = *set_appoggio;
 
-    rete_neurale = caricaReteNeurale(file_pesi);
-    if (rete_neurale == NULL) {
+    ReteNeurale *rete_appoggio = caricaReteNeurale(file_pesi);
+    if (rete_appoggio == NULL) {
         printf("Errore nel caricare il modello\n");
         return;
     }
+    rete_neurale = *rete_appoggio;
 
-    int indice_set = rand() % set->size;
+    free(set_appoggio);
+    free(rete_appoggio);
+
+    //Trasformo tutto in 0 e 1
+
+    /* for(int indice_immagine = 0; indice_immagine < set.size; indice_immagine++) {
+        for(int indice_byte = 0; indice_byte < N_INPUTS; indice_byte++) {
+            if(set.istanze[indice_immagine].dati[indice_byte] == 0)
+                set.istanze[indice_immagine].dati[indice_byte] = 0;
+            else
+                set.istanze[indice_immagine].dati[indice_byte] = 255;
+        }
+    } */
+
+    int indice_set = rand() % set.size;
 
     // Carica la prima immagine
     carica_immagine(indice_set);
 
     while(!key[KEY_ESC]) {
         disegna_interfaccia();
-        indice_set = rand() % set->size;
+        indice_set = rand() % set.size;
         evento_click_bottone(indice_set);
         rest(10);
     }
@@ -94,7 +109,7 @@ void init_allegro() {
 void carica_immagine(int indice_set)
 {
     // Stampa informazioni sull'immagine
-    printf("Immagine indice: %d, valore: %d. è un 7? %d\n", indice_set, set->istanze[indice_set].categoria, prevedi(indice_set));
+    printf("Immagine indice: %d, valore: %d. è un 7? %d\n", indice_set, set.istanze[indice_set].classificazione, prevedi(indice_set));
 
     // Itera su ogni pixel dell'immagine
     for (int y = 0; y < IMAGE_HEIGHT; y++)
@@ -102,7 +117,7 @@ void carica_immagine(int indice_set)
         for (int x = 0; x < IMAGE_WIDTH; x++)
         {
             // Ottieni il valore del pixel (scala di grigi, quindi un solo canale)
-            int gray_value = set->istanze[indice_set].immagine[y * IMAGE_WIDTH + x];
+            int gray_value = set.istanze[indice_set].dati[y * IMAGE_WIDTH + x];
 
             // Converti il valore in scala di grigi in un colore RGB (r = g = b = gray_value)
             int color = makecol(gray_value, gray_value, gray_value);
@@ -169,20 +184,12 @@ void evento_click_bottone(int indice_set)
 
 int prevedi(int indice_set)
 {
-    double **sigmoidi = (double **)malloc(sizeof(double *) * rete_neurale->size);
+    
+    double **sigmoidi = elabora_funzioni_attivazione(rete_neurale, set.istanze[indice_set], 2);
+    
+    byte output_corretto = get_out_corretto(set.istanze[indice_set].classificazione);
 
-    sigmoidi[0] = (double *)malloc(sizeof(double) * rete_neurale->layers[0].size);
-    sigmoidi[0] = funzioni_attivazione_layer_byte(rete_neurale->layers[0], set->istanze[indice_set].immagine);
-
-    for (int j = 1; j < rete_neurale->size; j++)
-    {
-        sigmoidi[j] = (double *)malloc(sizeof(double) * rete_neurale->layers[j].size);
-        sigmoidi[j] = funzioni_attivazione_layer_double(rete_neurale->layers[j], sigmoidi[j - 1]);
-    }
-
-    byte output_corretto = get_out_corretto(set->istanze[indice_set].categoria);
-
-    return previsione(sigmoidi[rete_neurale->size - 1][0]);
+    return previsione(sigmoidi[rete_neurale.size - 1][0]);
 }
 
 byte get_out_corretto(byte categoria) {