Lezioni/Lezione 3 - N-Gram Language Model.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"FW3kRiAFOf_S"},"source":["# **Aumentiamo il contesto: N-Gram Language Model**\n","### **Sviluppo di Makemore, Parte 2**\n","Dennis Remigio, Luglio 2024\n","\n","**Obiettivi**: Sviluppare e addestrare un N-Gram Language Model capace di generare nomi. Fornire basi su concetti fondamentali come l'embedding, batching, learning rate e modifica di parametri e iperparametri.\n","\n","**Precompetenze**: basi di Python, comprensione di base della struttura delle reti neurali e del loro addestramento, conoscenza del metodo di one hot encoding, conoscenza delle regole di broadcasting  \n","\n","&nbsp;\n","\n","---\n","\n","Adattamento e approfondimento della lezione di **Andrej Karpathy, PhD**: *Building makemore Part 2: MLP*, disponibile [qui](https://youtu.be/TCH_1BHY58I?si=jBRFtd6GilOJgB5F).\n","\n","---"]},{"cell_type":"markdown","metadata":{"id":"Ey7xebb_abm2"},"source":["## **Indice**\n","\n","[**Introduzione**](#s0)  \n","[**1 - Struttura della rete**](#s1)  \n","[**2 - Embedding**](#s2)  \n","&nbsp;&nbsp;&nbsp;&nbsp;[**2.1 - Encoding e decoding**](#s2.1)  \n","&nbsp;&nbsp;&nbsp;&nbsp;[**2.2 - Contesto e labelling**](#s2.2)  \n","&nbsp;&nbsp;&nbsp;&nbsp;[**2.3 - Tabella di look-up**](#s2.3)  \n","[**3 - Definizione dello strato nascosto**](#s3)  \n","&nbsp;&nbsp;&nbsp;&nbsp;[**3.1 - Moltiplicazione tra input e pesi: funzione `cat()`**](#s3.1)  \n","&nbsp;&nbsp;&nbsp;&nbsp;[**3.2 - Moltiplicazione tra input e pesi: funzione `view()`**](#s3.2)  \n","[**4 - Output layer, logits e calcolo della perdita**](#s4)  \n","[**5 - Efficientamento del codice**](#s5)  \n","&nbsp;&nbsp;&nbsp;&nbsp;[**5.1 - Calcolo della perdita: funzione `cross_entropy()`**](#s5.1)  \n","[**6 - Addestramento:**](#s6)  \n","&nbsp;&nbsp;&nbsp;&nbsp;[**6.1 - Batching**](#s6.1)  \n","&nbsp;&nbsp;&nbsp;&nbsp;[**6.2 - Learning rate**](#s6.2)  \n","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[**6.2.1 - Learning rate decay**](#s6.2.1)  \n","&nbsp;&nbsp;&nbsp;&nbsp;[**6.3 - Scomposizione del dataset in Train, Validation e Test**](#s6.3)  \n","&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[**6.3.1 - Grafico della matrice di embedding post addestramento**](#s6.3.1)  \n","[**7 - Generazione di nomi**](#s7)  \n","[**Conclusioni**](#s8)  "]},{"cell_type":"markdown","metadata":{"id":"Ejw3vRSaQRAy"},"source":["<a name=\"s0\"></a>\n","## **Introduzione**\n","\n","In questa lezione, seguendo l'articolo [***A Neural Probabilistic Language Model*** *(Bengio et al. 2003)*](https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf), creeremo un N-Gram Language Model.  \n","\n","La lezione segue e approfondisce i concetti affrontati nella lezione precedente (in cui abbiamo creato un **Bigram Language Model**, sia attraverso il conteggio di tutte le combinazioni possibili dei bigram, sia tramite l'addestramento di una rete neurale). In questa lezione approfondiremo un possibile metodo per far considerare al modello più di due caratteri alla volta, riducendo la perdita del modello e ottenendo generazioni qualitativamente migliori.\n","\n","Nella lezione precedente, abbiamo visto che era possibile creare un modello senza addestrare una rete neurale, ma individuando tutte le combinazioni dei bigram all'interno del dataset fornito, e calcolando quindi una distribuzione di probabilità. In questa lezione, ciò diventerebbe rapidamente impraticabile, poiché, per ogni carattere aggiunto come contesto, le combinazioni aumentano esponenzialmente.  \n","\n","Creeremo dunque un MLP (Multy-Layer Perceptron), procedendo poi con l'ottimizzazione dei gradienti per ridurre la perdita del modello.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"3pFXn02vqrGf"},"source":["Il funzionamento del modello di linguaggio che svilupperemo può essere spiegato attraverso i seguenti punti:\n","\n","1. **Individuazione degli elementi**: inizialmente, identifichiamo tutti gli elementi di base del nostro modello. Nel caso specifico di un modello basato sui caratteri, questi elementi sono i singoli caratteri dell'alfabeto, per un totale di 27 caratteri (includendo il carattere che definisce l'inizio e la fine del nome).  \n","\n","2. **Embedding**: il prossimo passo è rappresentare ciascun carattere come un punto in uno spazio vettoriale multidimensionale. Questo processo è noto come embedding. Ad esempio, possiamo immaginare uno spazio di due dimensioni dove ogni carattere viene posizionato come un punto specifico in un piano cartesiano. Inizialmente la rappresentazione viene effettuata in modo casuale.\n","\n","3. **Ottimizzazione e relazioni**: durante l'addestramento del modello, avviene il processo di ottimizzazione (forward pass e back propagation per la minimizzazione della perdita) in cui il modello apprende le relazioni tra i vari caratteri. Questo significa che i caratteri che tendono ad apparire vicini tra loro in un testo (come \"t\" e \"h\" nella parola \"the\") verranno rappresentati come punti vicini nello spazio vettoriale. Il modello modifica quindi la posizione dei punti nello spazio per riflettere queste relazioni.\n","\n","4. **Previsione**: una volta addestrato, il modello può fare previsioni anche su sequenze di caratteri che non ha visto durante l'addestramento. Quando incontra un carattere sconosciuto, il modello può guardare i punti vicini nello spazio vettoriale e utilizzare queste informazioni per effettuare una previsione il più possibile coerente.\n","\n","&nbsp;\n","\n","---\n","\n","Questo approccio vale allo stesso modo considerando le parole al posto dei caratteri. In tale caso, avverrà il word embedding (al posto del character embedding), in cui saranno le parole ad essere trasformate in punti nello spazio vettoriale, e parole con significati simili, o che vengono utilizzate in contesti simili, si troveranno relativamente vicine in tale spazio.\n","\n","---"]},{"cell_type":"markdown","metadata":{"id":"UcOwkRNorcUK"},"source":["<a name=\"s1\"></a>\n","## **1 - Struttura della rete**\n","\n","La rete che vogliamo creare è strutturata come segue:\n","\n","1. **Input**: nel nostro caso saranno 27, ovvero il numero dei caratteri, includendo anche il carattere speciale che indica il termine\n"," e l'inizio della parola (nel documento, che invece è strutturato in base alle parole che costituiscono il vocabolario, sono oltre 17000).  \n","\n","2. **Tabella di look-up**: ogni carattere verrà convertito in un vettore di embedding, di N dimensioni (il numero di dimensioni verrà scelto da noi, generalmente vengono utilizzate più dimensioni man mano che gli input sono numerosi, nel nostro caso, probabilmente, sarà sufficiente utilizzare 2 o 3 dimensioni). Se, ad esempio, utilizzassimo 2 dimensioni, ci sarebbe una tabella composta da 27 righe e 2 colonne, e ad ogni carattere corrisponderà una riga che rappresenterà il suo vettore di embedding. In questo esempio, tale passaggio porterebbe ad avere `27*2 = 54` neuroni per il primo input layer. La matrice `C` (ovvero la tabella di look-up) è condivisa tra i vari caratteri, in quanto ogni carattere avrà un suo embedding vector.\n","\n","3. **Primo strato nascosto**: la dimensione di questo strato è un iperparametro (hyperparameter), ovvero un parametro a discrezione del designer della rete neurale. La modifica di questo iperparametro può portare a differenti risultati, quindi andranno effettuati diversi test per ottimizzare i risultati (poi vedremo come). Questo layer è completamente connesso al layer di input, perciò ogni neurone di questo strato riceverà tutti gli input dallo strato di input.\n","\n","4. **Output layer**: la dimensione sarà data, nel nostro caso, dal numero di caratteri, ovvero 27 (nel caso delle parole, sarebbe stata di oltre 17000). L'output seguirà la funzione SoftMax che trasformerà i valori in una distribuzione di probabilità.  \n","Nella fase di training, ogni carattere avrà un suo label (etichetta), che fornirà al modello l'informazione su quale probabilità andrebbe aumentata per ridurre la perdita e migliorare le prestazioni tramite backpropagation e ottimizzazione dei gradienti.\n","\n","&nbsp;\n","\n","![MLP.png](data:image/png;base64,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)\n","\n","\n","I parametri di questa rete saranno i pesi e i bias del layer nascosto, e i pesi e i bias dell'output layer.  \n"]},{"cell_type":"markdown","metadata":{"id":"eBytN3SzKKtW"},"source":["<a name=\"s2\"></a>\n","## **2 - Embedding**\n","\n","In questo capitolo ci focalizzeremo su tutto il processo necessario ad effettuare l'embedding dei caratteri. Inizialmente, faremo in modo di convertire le lettere in numeri (processo di encoding), poi proseguiremo con il definire la matrice `X` degli input e il vettore `Y` di ciò che si vuole prevedere (ovvero l'etichetta corrispondente a ciascun input che, nel caso del Bigram Language Model era un singolo carattere, mentre in questo caso è l'insieme di caratteri definiti dal contesto scelto). \n","\n","Infine, si passerà ad effettuare il processo di embedding vero e proprio, ovvero a convertire ciascun carattere in un vettore, nel nostro caso di due elementi (poiché, inizialmente, effettueremo le varie prove effettuando un embedding in sole 2 dimensioni). Inizialmente, i valori di ciascun vettore saranno definiti in maniera casuale, ma, a seguito dell'addestramento, i valori cambieranno, indicando che il modello ha individuato delle relazioni tra i vari caratteri. Analizzando i vari vettori sarà infatti possibile notare che caratteri simili (per natura, per modalità di utilizzo o altre ragioni) avranno vettori con valori simili. Nel nostro caso, visto che stiamo effetuando un embeddin in vettori di due elementi, potremo rappresentare i vettori di ciascun carattere in un piano cartesiano e vedere in maniera diretta come il modello considera i vari caratteri (queste osservazioni le faremo nelle ultime parti della lezione)."]},{"cell_type":"markdown","metadata":{"id":"giBqwSCtxoPK"},"source":["<a name=\"s2.1\"></a>\n","### **2.1 - Encoding e decoding**\n","\n","Iniziamo importando le librerie fondamentali"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vowXRstocd8F"},"outputs":[],"source":["import torch\n","import torch.nn.functional as F\n","import matplotlib.pyplot as plt # per generare grafici\n","%matplotlib inline"]},{"cell_type":"markdown","metadata":{"id":"i4XUUZ2NVFJA"},"source":["Importiamo successivamente il file che utilizzeremo per questa lezione, ovvero lo stesso della lezione precedente (lista di nomi)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":434,"status":"ok","timestamp":1719600814177,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"9Rlc3YeWcm_d","outputId":"628b6e7e-13e4-4898-9fed-337210a40815"},"outputs":[{"name":"stdout","output_type":"stream","text":["--2024-06-28 18:53:33--  https://raw.githubusercontent.com/karpathy/makemore/master/names.txt\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 228145 (223K) [text/plain]\n","Saving to: ‘names.txt’\n","\n","\rnames.txt             0%[                    ]       0  --.-KB/s               \rnames.txt           100%[===================>] 222.80K  --.-KB/s    in 0.02s   \n","\n","2024-06-28 18:53:33 (10.3 MB/s) - ‘names.txt’ saved [228145/228145]\n","\n"]}],"source":["# download the names.txt file from github\n","!wget https://raw.githubusercontent.com/karpathy/makemore/master/names.txt"]},{"cell_type":"markdown","metadata":{"id":"Ri0LhgNqydg6"},"source":["Trasformiamo la lista di nomi in un vettore in cui ogni nome costituisce un elemento."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1719600814178,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"uH2MGP4FcidR","outputId":"d5b65e39-f2d3-456a-db32-20f3e68c01e6"},"outputs":[{"data":{"text/plain":["['emma', 'olivia', 'ava', 'isabella', 'sophia', 'charlotte', 'mia', 'amelia']"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["words = open('names.txt', 'r').read().splitlines()\n","words[:8]"]},{"cell_type":"markdown","metadata":{"id":"j7Np5fawymhr"},"source":["Possiamo vedere che sono presenti 32033 nomi in questo dataset."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1719600814178,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"FbakoEqAcvnF","outputId":"5bae1ace-4855-4ea4-8699-2f4f0d6b1b04"},"outputs":[{"data":{"text/plain":["32033"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["len(words)"]},{"cell_type":"markdown","metadata":{"id":"iCXj7ayYyvQV"},"source":["Come fatto per il Bigram Language Model, creiamo il codice per l'encoding e il decoding di ciascun carattere (molto sempicmenente, in numeri da `1` a `27`, includendo anche il `.` come carattere numero `0`)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1719600814517,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"EYnXhmWVcx-l","outputId":"689560a9-d240-4666-f863-16fcef94def7"},"outputs":[{"name":"stdout","output_type":"stream","text":["{1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n"]}],"source":["# build the vocabulary of characters and mappings to/from integers\n","chars = sorted(list(set(''.join(words))))\n","stoi = {s:i+1 for i,s in enumerate(chars)}\n","stoi['.'] = 0\n","itos = {i:s for s,i in stoi.items()}\n","print(itos)"]},{"cell_type":"markdown","metadata":{"id":"FHvEGptT1pbT"},"source":["<a name=\"s2.2\"></a>\n","### **2.2 - Contesto e labelling**\n","\n","Ora vediamo come abbiamo intenzione di etichettare i valori. In questo esempio, utilizzeremo `3` caratteri come contesto per la generazione del quarto (rispetto al bigram language model che ne aveva solo `1`, questo dovrebbe portare ad un netto miglioramento delle prestazioni del modello)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1719600815835,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"MB5XgJxdczFi","outputId":"4f8daac7-cda8-40d4-b047-c3362627630d"},"outputs":[{"name":"stdout","output_type":"stream","text":["emma\n","... ---> e\n","..e ---> m\n",".em ---> m\n","emm ---> a\n","mma ---> .\n","olivia\n","... ---> o\n","..o ---> l\n",".ol ---> i\n","oli ---> v\n","liv ---> i\n","ivi ---> a\n","via ---> .\n","ava\n","... ---> a\n","..a ---> v\n",".av ---> a\n","ava ---> .\n"]}],"source":["# build the dataset\n","block_size = 3 # contesto: quanti caratteri consideriamo per poter prevedere il seguente\n","\n","X, Y = [], []\n","for w in words[:3]: # per l'esempio consideriamo solamente le prime 3 parole\n","\n","  print(w) # scrive la parola corrente per intero\n","  context = [0] * block_size # inizializziamo i caratteri di contesto con un vettore di 3 elementi (inizialmente [0, 0, 0], ovvero 3 volte il carattere '.')\n","  for ch in w + '.': # per ogni carattere di una data parola (alla quale viene aggiunto manualmente un punto alla fine)\n","    ix = stoi[ch] # viene individuato il numero del carattere corrente (valore dato dall'encoding)\n","    X.append(context) # aggiungiamo ad X il valore del contesto attuale (inizialmente quindi si avrà [0, 0, 0])\n","    Y.append(ix) # aggiungiamo ad Y il numero del carattere corrente\n","    print(''.join(itos[i] for i in context), '--->', itos[ix]) # scrive le lettere presenti nel contesto (quindi inizialmente converte [0, 0, 0] in '...'), seguita da una freccia e la lettera corrente\n","    context = context[1:] + [ix] # viene tagliato il primo elemento del contesto , e si fa l'append con il numero del carattere attuale (quindi per il primo giro in questo esempio si passa da [0, 0, 0] a [0, 0, 5])\n","\n","X = torch.tensor(X)\n","Y = torch.tensor(Y)"]},{"cell_type":"markdown","metadata":{"id":"bgjh8nB8XEtE"},"source":["Possiamo analizzare i dati attuali, ottenuti considerando solamente le prime `3` parole."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":322,"status":"ok","timestamp":1719600817957,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"egqlDj1jZZoR","outputId":"823ed7f6-76a6-4b65-926b-3750b9229b43"},"outputs":[{"data":{"text/plain":["(torch.Size([16, 3]), torch.int64, torch.Size([16]), torch.int64)"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["X.shape, X.dtype, Y.shape, Y.dtype"]},{"cell_type":"markdown","metadata":{"id":"d5kBwqzKZiSu"},"source":["Vediamo che il dataset è formato da `16` esempi (considerando solo le prime `3` parole), ed ogni input `X` alla rete neurale è fatto da `3` numeri interi (`int`).  \n","\n","È inoltre presente `Y`, che rappresenta i label, che sono in totale `16`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1719600819092,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"sn0SnC8GZ5wk","outputId":"bda20b08-5862-4fc1-e67c-e5586d85be44"},"outputs":[{"data":{"text/plain":["tensor([[ 0,  0,  0],\n","        [ 0,  0,  5],\n","        [ 0,  5, 13],\n","        [ 5, 13, 13],\n","        [13, 13,  1],\n","        [ 0,  0,  0],\n","        [ 0,  0, 15],\n","        [ 0, 15, 12],\n","        [15, 12,  9],\n","        [12,  9, 22],\n","        [ 9, 22,  9],\n","        [22,  9,  1],\n","        [ 0,  0,  0],\n","        [ 0,  0,  1],\n","        [ 0,  1, 22],\n","        [ 1, 22,  1]])"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["X"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1,"status":"ok","timestamp":1719600820172,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"J7W94OnIZ7_h","outputId":"88999e0d-4acb-43b1-d318-a994853befbc"},"outputs":[{"data":{"text/plain":["tensor([ 5, 13, 13,  1,  0, 15, 12,  9, 22,  9,  1,  0,  1, 22,  1,  0])"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["Y"]},{"cell_type":"markdown","metadata":{"id":"2aXIm_33aI-0"},"source":["Costruiamo quindi una rete neurale che prende in input le `X` e predice le `Y`."]},{"cell_type":"markdown","metadata":{"id":"6mjNzoY4Luki"},"source":["<a name=\"s2.3\"></a>\n","### **2.3 - Tabella di look-Up**\n","\n","Nel documento da cui stiamo prendendo riferimento, viene effettuato l'embedding di 17000 parole in uno spazio vettoriale di 30 dimensioni. Nel nostro caso, poiché abbiamo solamente 27 caratteri, effettueremo un embedding in uno spazio vettoriale di sole 2 dimensioni.  \n","\n","L'obiettivo di effettuare l'embedding è di scomporre una informazione in più parti (sarebbe come scomporre un determinato colore nei colori primari che lo compongono) in modo che il sistema, tramite l'addestramento, possa comprendere come l'elemento è relazionato ad altri.\n","\n","Definiamo ora la tabella di look up (`C`), che inizialmente ha valori casuali al suo interno, e verrà in seguito ottimizzata tramite l'addestramento."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":441,"status":"ok","timestamp":1719600821693,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"uTVpMQiPcUJj","outputId":"540061bc-e899-46fb-a17d-8a1be3657a68"},"outputs":[{"data":{"text/plain":["tensor([[ 0.1365,  1.9290],\n","        [ 0.6803, -0.6909],\n","        [-0.2438,  0.1504],\n","        [ 0.4734, -1.6406],\n","        [ 1.1636, -0.3852],\n","        [ 3.1188,  0.9540],\n","        [-0.5584,  0.9775],\n","        [-1.8017, -0.4775],\n","        [-0.5382, -0.8392],\n","        [-1.0231, -1.0667],\n","        [-0.8715,  0.4696],\n","        [ 1.4611,  0.2253],\n","        [-0.0694, -0.9621],\n","        [ 1.2248, -1.0539],\n","        [ 1.4987,  0.5401],\n","        [ 0.2790,  0.1146],\n","        [ 1.4451,  0.1103],\n","        [ 0.0339,  0.7400],\n","        [-0.4949,  1.1871],\n","        [-0.8520,  0.0759],\n","        [-0.6980, -0.5812],\n","        [ 2.1201,  0.3198],\n","        [-0.1074,  1.7135],\n","        [ 0.6164, -0.8577],\n","        [ 1.0939, -0.1937],\n","        [ 0.3074, -3.6817],\n","        [-0.2625, -1.2257]])"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["C = torch.randn((27, 2))\n","C"]},{"cell_type":"markdown","metadata":{"id":"lrtretCzcwQN"},"source":["Prima di effettuare l'embedding di tutte le lettere, proviamo con una sola, in modo da capire bene come funziona il processo.\n","\n","Nella lezione precedente abbiamo visto come sia possibile effettuare inizialmente un encoding (one hot encoding) dei caratteri, e poi effettuare il prodotto vettoriale con `C`.\n","\n","Facciamo un esempio con il quinto carattere:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":305,"status":"ok","timestamp":1719600823979,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"d0gE62pwd02H","outputId":"0f6802e1-add6-4fb8-dbe4-2d2f567ddcf7"},"outputs":[{"data":{"text/plain":["tensor([3.1188, 0.9540])"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["(F.one_hot(torch.tensor(5), 27)).float() @ C # one hot encoding sul numero 5, in un vettore di 27 elementi, viene poi reso float e viene fatto il prodotto vettoriale con \"C\""]},{"cell_type":"markdown","metadata":{"id":"XPNcpfMLeJVn"},"source":["Per via della struttura del vettore ottenuto con il one hot encoding, questo passaggio equivale a selezionare il quinto valore di `C`, come possiamo vedere facilmente:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1719600824950,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"hrwnV7N7eRYB","outputId":"1817f4a2-54bf-4ee7-f951-42be5622fa25"},"outputs":[{"data":{"text/plain":["tensor([3.1188, 0.9540])"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["C[5]"]},{"cell_type":"markdown","metadata":{"id":"I0JCKeUxenpY"},"source":["Poiché il risultato è equivalente, utilizzeremo questo secondo metodo, in quanto è più rapido.\n","\n","&nbsp;\n","\n","---\n","\n","Questo confronto ci permette di capire che la fase di embedding può essere considerata anche come un primo layer della rete, che riceve in input i caratteri (a seguito del processo di one hot encoding), e moltiplica i \"pesi\" dati dalla matrice `C`.\n","\n","---\n","\n","&nbsp;\n","\n","Ora possiamo pensare a come effettuare l'embedding per tutti gli input `X`. Per via della struttura di `C` (che, ricevendo in input il numero del carattere, restituisce direttamente il valore della riga corrispondente della matrice `C`), si può fare molto semplicemente."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":308,"status":"ok","timestamp":1719600828754,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"QHhK17S7qIrW","outputId":"044bf924-d15c-4078-c281-1a8bf7837ef4"},"outputs":[{"data":{"text/plain":["tensor([[[ 0.1365,  1.9290],\n","         [ 0.1365,  1.9290],\n","         [ 0.1365,  1.9290]],\n","\n","        [[ 0.1365,  1.9290],\n","         [ 0.1365,  1.9290],\n","         [ 3.1188,  0.9540]],\n","\n","        [[ 0.1365,  1.9290],\n","         [ 3.1188,  0.9540],\n","         [ 1.2248, -1.0539]],\n","\n","        [[ 3.1188,  0.9540],\n","         [ 1.2248, -1.0539],\n","         [ 1.2248, -1.0539]],\n","\n","        [[ 1.2248, -1.0539],\n","         [ 1.2248, -1.0539],\n","         [ 0.6803, -0.6909]],\n","\n","        [[ 0.1365,  1.9290],\n","         [ 0.1365,  1.9290],\n","         [ 0.1365,  1.9290]],\n","\n","        [[ 0.1365,  1.9290],\n","         [ 0.1365,  1.9290],\n","         [ 0.2790,  0.1146]],\n","\n","        [[ 0.1365,  1.9290],\n","         [ 0.2790,  0.1146],\n","         [-0.0694, -0.9621]],\n","\n","        [[ 0.2790,  0.1146],\n","         [-0.0694, -0.9621],\n","         [-1.0231, -1.0667]],\n","\n","        [[-0.0694, -0.9621],\n","         [-1.0231, -1.0667],\n","         [-0.1074,  1.7135]],\n","\n","        [[-1.0231, -1.0667],\n","         [-0.1074,  1.7135],\n","         [-1.0231, -1.0667]],\n","\n","        [[-0.1074,  1.7135],\n","         [-1.0231, -1.0667],\n","         [ 0.6803, -0.6909]],\n","\n","        [[ 0.1365,  1.9290],\n","         [ 0.1365,  1.9290],\n","         [ 0.1365,  1.9290]],\n","\n","        [[ 0.1365,  1.9290],\n","         [ 0.1365,  1.9290],\n","         [ 0.6803, -0.6909]],\n","\n","        [[ 0.1365,  1.9290],\n","         [ 0.6803, -0.6909],\n","         [-0.1074,  1.7135]],\n","\n","        [[ 0.6803, -0.6909],\n","         [-0.1074,  1.7135],\n","         [ 0.6803, -0.6909]]])"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["emb = C[X]\n","emb"]},{"cell_type":"markdown","metadata":{"id":"5umi7Hvyrvm-"},"source":["Possiamo infatti vedere che `emb` è strutturato come un vettore di dimensione `16x3x2` (`16` è dato dal numero di esempi `X`, `3` è dato dal numero di lettere di contesto e `2` è dato dalla dimensionalità che abbiamo scelto per l'embedding)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1,"status":"ok","timestamp":1719600830910,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"XO0KJtF6sDjw","outputId":"4385f843-da6f-45c7-ecc2-2a7671fae32a"},"outputs":[{"data":{"text/plain":["torch.Size([16, 3, 2])"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["emb.shape"]},{"cell_type":"markdown","metadata":{"id":"2on5mqO2NK7k"},"source":["<a name=\"s3\"></a>\n","## **3 - Definizione dello strato nascosto**"]},{"cell_type":"markdown","metadata":{"id":"oUBOQNeesNnp"},"source":["<a name=\"s3.1\"></a>\n","### **3.1 - Moltiplicazione tra input e pesi: funzione `cat()`**\n","\n","Procediamo ora con la definizione dell'hidden layer, con pesi inizializzati in maniera casuale. Il numero di pesi dipende dall'input: nel nostro caso, ogni neurone riceverà `3x2 = 6` input (poiché il contesto è di `3` caratteri, e stiamo effettuando un embedding in `2` dimensioni)."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"arQrF2igsUj_"},"outputs":[],"source":["W1 = torch.randn((6, 100)) # 100 indica l'iperparametro arbitrario relativo al numero di neuroni dell'hidden layer\n","b1 = torch.randn(100) # bias, anch'essi inizializzati casualmente, sono 100, uno per ogni neurone del layer"]},{"cell_type":"markdown","metadata":{"id":"RGrNOSkKt_IC"},"source":["Ora vorremmo quindi effettuare l'operazione `emb @ W1 + b1`, ma non possiamo farla, in quanto il prodotto vettoriale non può funzionare tra un vettore di `3` dimensioni (`emb`) e uno di `2` (`W1`).  \n","\n","Ci serve quindi un modo per schiacciare la terza dimensione di `emb`, concatenando i valori in qualche modo (trasformandolo da `(16, 3, 2)` in `(16, 6)`.\n","\n","Fortunatamente, `PyTorch` ha una funzione che fa esattamente questo, ovvero la funzione `cat`.\n","\n","&nbsp;\n","\n","---\n","\n","Cerchiamo inizialmente di capire cosa vorremmo ottenere, poi generalizzeremo il processo, per permettere al codice di funzionare a prescindere dal numero di caratteri scelti come contesto.\n","\n","Vogliamo concatenare il valore `0`, il valore `1` e il valore `2` del secondo elemento di `emb` (in questo caso sono solamente `3`, in quanto abbiamo solamente `3` caratteri come contesto)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1719600833654,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"sTKCL5Olvy0H","outputId":"fabc3797-a437-4fa1-94d2-3d75770ce84a"},"outputs":[{"data":{"text/plain":["torch.Size([16, 6])"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["torch.cat([emb[:, 0, :], emb[:, 1, :], emb[:, 2, :]], 1).shape # l'1 finale è perché vogliamo concatenare sulla dimensione 1, non la 0."]},{"cell_type":"markdown","metadata":{"id":"RGm6bxRItJfU"},"source":["Come previsto, in questo modo otterremmo il risultato desiderato.\n","\n","---\n","\n","&nbsp;\n","\n","Possiamo ora generalizzare il processo, in quanto, attualmente, se aumentassimo i caratteri di contesto dovremmo aggiungere pezzi al codice (ovvero, altri `emb[:, x, :]`).\n","\n","Per generalizzare il codice, possiamo utilizzare `torch.unbind(emb, 1)`, che restituisce una lista di tensori, separati rispetto alla dimensione indicata (nel nostro caso, la dimensione `1`, ovvero le righe)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1719600834776,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"p-_tLzGUvLcD","outputId":"9078f3e8-f5b8-4264-f407-a3407810958f"},"outputs":[{"data":{"text/plain":["torch.Size([16, 6])"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["torch.cat(torch.unbind(emb, 1), 1).shape"]},{"cell_type":"markdown","metadata":{"id":"CbVSJW2BvgGP"},"source":["Come previsto, la forma è la stessa, ma ora è generalizzato a qualsiasi scelta del numero di caratteri di contesto.\n","\n","Si da il caso che però esista un modo estremamente più efficiente di svolgere la stessa operazione."]},{"cell_type":"markdown","metadata":{},"source":["<a name=\"s3.2\"></a>\n","### **3.2 - Moltiplicazione tra input e pesi: funzione `view()`**\n","\n","Per comprendere quanto segue, è necessario sapere che, per ogni tensore, viene creato uno storage di fondo, che è rappresentato come un vettore monodimensionale che ha al suo interno tutto il contenuto del tensore. E così che viene salvato nella memoria del computer.\n","\n","`PyTorch` ha una funzione, chiamata `view()`, che permette di vedere un tensore nella forma che decidiamo, senza che venga effettuato alcun calcolo, quindi senza creare, modificare, copiare o spostare alcuna memoria. L'unica cosa che viene cambiata è la modalità di visualizzazione del tensore.\n","\n","Possiamo quindi utilizzare questa funzione, al posto di utilizzare funzioni per concatenare. L'unica accortezza che va mantenuta sta nel fare in modo che il numero totale di elementi del tensore sia lo stesso, calcolabile facilmente tramite moltiplicazione *(`16x3x2` potrebbe essere convertito in `16x6` e non, ad esempio, in `16x4`)*."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1719600836527,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"L6iNqdzRy4xz","outputId":"f9db217f-10e6-4f48-f35b-53ef707905be"},"outputs":[{"data":{"text/plain":["torch.Size([16, 6])"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["emb.view(16, 6).shape # view permette di specificare la forma in cui vogliamo vedere il tensore, nel nostro caso vogliamo che sia (16, 6)"]},{"cell_type":"markdown","metadata":{"id":"Qqil0tw3zPru"},"source":["Possiamo constatare che il risultato è lo stesso confrontando i due metodi direttamente:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1719600837452,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"YI1P5A2VzU3F","outputId":"d4bc6744-11ff-48e6-bcae-34f9077014f6"},"outputs":[{"data":{"text/plain":["tensor([[True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True],\n","        [True, True, True, True, True, True]])"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["emb.view(16, 6) == torch.cat(torch.unbind(emb, 1), 1)"]},{"cell_type":"markdown","metadata":{"id":"ipW4PWq3zouW"},"source":["Come previsto, il risultato è identico.\n","\n","Possiamo ora moltiplicare gli input per i pesi, e in seguito addizionare i bias."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":288,"status":"ok","timestamp":1719600839673,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"ym704JAuz3Vq","outputId":"9ff1c9aa-375e-4586-e74b-c67a2fc57f1a"},"outputs":[{"data":{"text/plain":["torch.Size([16, 100])"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["h = emb.view(16,6) @ W1 + b1\n","h.shape"]},{"cell_type":"markdown","metadata":{"id":"myEh5qCc0UNB"},"source":["In questo modo, il codice funziona come sperato. Per generalizzare ancora di più, possiamo fare in modo di non dover nemmeno indicare il numero di input che arrivano al neurone (in questo caso `16`). Per farlo, è sufficiente indicare `emb.shape[0]`, oppure, utilizzando la proprietà di `view()` che impone la dimensione di essere la stessa del tensore di origine, possiamo scrivere semplicemente `-1`, e il sistema lo sostituirà in automatico nel valore necedsario a far tornare i conti."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":319,"status":"ok","timestamp":1719600841123,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"QFWVAY-F1kGB","outputId":"875e4f6b-c63b-4746-8f58-5c3a34614295"},"outputs":[{"data":{"text/plain":["torch.Size([16, 100])"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["h = emb.view(-1, 6) @ W1 + b1\n","h.shape"]},{"cell_type":"markdown","metadata":{"id":"IYZ74V_f2VmI"},"source":["&nbsp;\n","\n","---\n","\n","Ora, prima di continuare, dobbiamo fare attenzione ad un aspetto, che potrebbe passare inosservato e potenzialmente creare problemi in alcune situazioni.\n","\n","Ciò che osserveremo è l'addizione con il bias. Se osserviamo le dimensioni dei due tensori che si sommano, possiamo vedere che non hanno la stessa forma:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1719600842496,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"onaw72r_3ED0","outputId":"b2a46fad-d556-4306-8bb2-bbbb8e49f363"},"outputs":[{"data":{"text/plain":["torch.Size([16, 100])"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["(emb.view(-1, 6) @ W1).shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1719600842496,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"P-raqCoS3NT7","outputId":"0dbe4958-d43d-499a-e40b-2b7d42f887ef"},"outputs":[{"data":{"text/plain":["torch.Size([100])"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["b1.shape"]},{"cell_type":"markdown","metadata":{"id":"VR8G1PJQ3WeF"},"source":["Il fatto che non siano della stessa forma fa sì che avvenga il `broadcasting` (che abbiamo studiato nella lezione precedente).\n","\n","Ricordiamo quindi che, prima vengono allineati a destra, poi verrà creata una dimensione fittizia, verrà copiato verticalmente lo stesso valore (quindi su tutte le righe) e, infine, verrà fatta l'addizione riga per riga.  \n","\n","In questo caso, va bene che accada ciò. Tuttavia, va tenuto in mente, perché in alcuni casi potrebbe generare problemi difficili da diagnosticare.\n","\n","---\n","\n","&nbsp;"]},{"cell_type":"markdown","metadata":{},"source":["<a name=\"s4\"></a>\n","## **4 - Output layer, logits e calcolo della perdita**\n","\n","Possiamo proseguire, definendo i pesi e i bias del layer di output."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oMT9oYZI4xrA"},"outputs":[],"source":["W2 = torch.randn((100, 27))\n","b2 = torch.randn(27)"]},{"cell_type":"markdown","metadata":{"id":"Ohu1fp5e5K6I"},"source":["Possiamo quindi calcolare i logits passando gli output dell'hidden layer a quest'ultimo."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ylSQ1J175FUU"},"outputs":[],"source":["logits = h @ W2 + b2"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1,"status":"ok","timestamp":1719600845233,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"J5XPceHN7KQE","outputId":"a784371d-5e75-464f-b543-ba6064b22ad6"},"outputs":[{"data":{"text/plain":["torch.Size([16, 27])"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["logits.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":312,"status":"ok","timestamp":1719600847044,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"XZXv4e7d7OHX","outputId":"88be85ed-412d-438e-a78c-c5519b4c54f7"},"outputs":[{"data":{"text/plain":["tensor([[-2.2333e+01,  1.6065e+01,  8.0756e+00, -3.5380e+00,  7.0736e+01,\n","         -3.8841e+00,  2.2342e+01, -1.7306e+00,  4.2941e+01,  4.5608e+01,\n","          5.0216e+01, -3.1148e+01,  5.0224e+01, -4.3079e+01, -2.1484e+01,\n","          1.1509e+01, -4.5220e+01, -1.0214e+01, -4.4118e+01,  3.7032e+01,\n","          6.2009e+00,  6.6579e+01,  2.9499e+01, -3.0824e+01,  1.7023e+01,\n","          4.3468e+01,  4.1271e+01],\n","        [ 4.6201e+01, -1.9390e+01,  2.0044e+00,  2.7820e+00, -2.8141e+01,\n","          5.8756e+00,  3.3826e+01, -1.0605e+01,  3.5985e+01, -3.4499e+01,\n","          9.6729e+01, -6.5787e+01,  1.1131e+01, -7.5060e+01, -3.3734e+01,\n","         -3.1330e+01, -3.4867e+01, -1.5719e+01, -3.4616e+01, -3.2702e+01,\n","          2.4746e+01,  6.7361e+01,  4.7126e+01, -5.8556e+01,  2.1590e+01,\n","          1.0713e+01, -3.5268e+01],\n","        [ 4.3577e+01, -2.9447e+01, -9.5175e+00, -6.3143e+01, -6.3936e+01,\n","          1.3883e+01,  7.7691e+01,  3.1018e+00,  6.6266e+01, -6.2514e+01,\n","          4.7908e+01, -7.0655e+01,  4.3329e+01, -5.9412e+01, -2.7549e+01,\n","         -5.2586e+01, -3.7147e+01, -3.3085e+01, -1.7055e+01,  2.1386e+01,\n","         -3.0711e+00,  5.4147e+01,  1.0818e+01,  7.4553e+00,  4.4768e+01,\n","          3.0226e+01,  1.2667e+00],\n","        [ 6.3289e+00,  3.5633e+01, -4.7927e+01, -2.5904e+01, 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2.2611e+01,  2.7088e+01,  2.0928e+00,  1.9853e+01,  7.4594e-01,\n","         -2.3331e+00,  1.1511e+01],\n","        [-1.9771e+01,  2.4299e+01, -1.6824e+01, -4.4427e+00,  2.8883e+01,\n","         -4.9262e-01,  2.7950e+01,  4.8522e+00, -2.4962e+01, -1.0005e+01,\n","         -2.5209e+01,  5.1923e+00, -3.9905e+00,  2.6893e+01,  8.7052e+00,\n","          1.0460e+01,  7.4526e+00,  2.5767e+00,  1.1271e+01,  1.7923e+01,\n","         -4.3923e+00, -1.1103e+01, -1.5296e+01,  3.2709e+01,  4.2706e+00,\n","         -2.6071e+01,  6.8653e+00],\n","        [-2.6851e+01,  3.4877e+01, -8.4907e+00,  3.9542e+01,  9.2947e+01,\n","         -1.5232e+01, -1.5972e+01,  4.6367e+00, -4.5425e+01,  3.6844e+01,\n","          1.0495e+01,  4.7983e+00, -1.8145e+01, -5.7083e+00,  2.0499e+01,\n","          4.3843e+01, -1.9218e+01,  3.3077e+01,  8.4953e+00,  4.9096e+00,\n","         -1.5162e+01,  4.1008e-01,  2.4149e+01, -2.0624e+01,  2.0238e-01,\n","         -2.1805e+00,  9.7295e+00],\n","        [-5.7596e+00, 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   5.0216e+01, -3.1148e+01,  5.0224e+01, -4.3079e+01, -2.1484e+01,\n","          1.1509e+01, -4.5220e+01, -1.0214e+01, -4.4118e+01,  3.7032e+01,\n","          6.2009e+00,  6.6579e+01,  2.9499e+01, -3.0824e+01,  1.7023e+01,\n","          4.3468e+01,  4.1271e+01],\n","        [ 1.1670e+01, -5.4577e+00,  4.5835e-01, -2.1377e+01, -2.2684e+01,\n","          7.7120e+00,  4.1828e+01, -6.6982e+00,  3.1081e+01, -1.4560e+01,\n","          3.0522e+01, -3.8425e+01,  2.5650e+01, -2.8409e+00, -2.5914e+01,\n","         -2.3873e+01, -1.2130e+01, -3.0690e+01, -4.2511e+01,  2.1771e+00,\n","          1.6760e+01,  4.9006e+01, -8.3813e-01, -6.7515e+00,  1.4522e+01,\n","         -2.9263e+00, -1.4276e+01],\n","        [-2.2766e+01,  2.2661e+01, -5.0473e+00,  7.3996e+00,  9.6409e+01,\n","         -8.0413e+00,  4.2100e+01,  4.9442e+00,  3.5929e+01,  1.0623e+01,\n","          4.0949e+01, -1.2143e+01,  4.9219e+01, -7.1444e+01, -1.3480e+01,\n","          2.8943e+01, -4.2443e+01,  1.4566e+01,  9.2497e+00,  4.8239e+01,\n","          1.5216e+01,  4.0279e+01,  4.5248e+01, -4.4370e+00,  3.2280e+00,\n","          4.7417e+01,  6.7489e+01],\n","        [ 9.3099e+00,  7.4896e+00, -1.1006e+01, -1.8035e+01, -3.4606e+01,\n","          9.7149e+00,  2.5726e+00, -1.5789e+00, -3.1444e+01, -2.0281e+00,\n","          1.2608e+01, -3.7348e+01, -3.3020e+01,  2.3554e+01,  3.5729e+00,\n","         -3.2963e+01,  1.5291e+00, -1.9874e+01, -3.2139e+01, -2.3325e+01,\n","         -2.6853e+01,  7.4235e+00, -2.0073e+01, -1.5978e+01,  4.1897e+01,\n","         -4.5280e+01, -5.2334e+01]])"]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["logits"]},{"cell_type":"markdown","metadata":{"id":"QyVNNVaS7dkY"},"source":["Come fatto nella lezione precedente, ora calcoleremo l'esponenziale per avere un conteggio fittizio, e poi calcoleremo le probabilità."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"G-UCRUhS7nEb"},"outputs":[],"source":["counts = logits.exp()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sWHwECjW7tJ8"},"outputs":[],"source":["prob = counts / counts.sum(1, keepdims = True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1719600850465,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"IGqYFiIz77wf","outputId":"5b277bbe-5dd5-461a-d27a-2f22223a8c49"},"outputs":[{"data":{"text/plain":["torch.Size([16, 27])"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["prob.shape"]},{"cell_type":"markdown","metadata":{"id":"19ARCBdt8Fru"},"source":["Ovviamente, ogni riga di `prob` somma a `1`:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1,"status":"ok","timestamp":1719600852192,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"Yei8V_F08JbJ","outputId":"e3377d76-9065-4193-bbd8-915ba397b4da"},"outputs":[{"data":{"text/plain":["tensor(1.)"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["prob[0].sum()"]},{"cell_type":"markdown","metadata":{"id":"wnCdfh6O9doM"},"source":["Ora possiamo vedere, a rete non addestrata, la probabilità che indovini le `Y`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1719600852997,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"DjZ8ThVU-tsa","outputId":"d601eb00-79fc-403e-fec6-3f6d3904564f"},"outputs":[{"data":{"text/plain":["tensor([3.8546e-33, 0.0000e+00, 0.0000e+00, 2.0926e-15, 2.9058e-17, 1.8672e-26,\n","        1.4282e-08, 6.8993e-36, 1.3723e-21, 0.0000e+00, 0.0000e+00, 8.8248e-17,\n","        1.7781e-24, 2.2531e-22, 0.0000e+00, 7.0414e-15])"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["prob[torch.arange(16), Y]"]},{"cell_type":"markdown","metadata":{"id":"SZ8X8YH0-7Fb"},"source":["Questo codice è strutturato come segue: `torch.arange(16)` crea un vettore che va da `0` a `15`. Poi, quindi, seleziona l'indice della riga di `logits`; poi è selezionato il relativo valore di `Y` che, poiché non ha ricevuto il one hot encoding, permette di scorrere lungo la riga di `logits` fino all'indice corrispondente. L'output è, quindi, la probabilità alla riga corretta, e alla colonna che corrisponde al label di `Y`.\n","\n","Ora, come effettuato per la lezione precedente, troviamo la perdita e calcoliamo il valore negativo del logaritmo della perdita media."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":322,"status":"ok","timestamp":1719600854764,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"J6c4YWUd__iM","outputId":"5f4974af-6d49-427a-e6cc-9b703b703533"},"outputs":[{"data":{"text/plain":["tensor(inf)"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["loss = -prob[torch.arange(16), Y].log().mean()\n","loss"]},{"cell_type":"markdown","metadata":{"id":"eCJS-UybZNzt"},"source":["Prima di proseguire, miglioriamo in codice, per renderlo più efficiente o organizzato."]},{"cell_type":"markdown","metadata":{},"source":["<a name=\"s5\"></a>\n","## **5 - Efficientamento del codice**\n","\n","Riscriviamo quindi tutto il codice, in maniera strutturata, a partire dal dataset completo:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1719600855519,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"hhAV6OmrZzWN","outputId":"ab8d021f-1548-49a3-fb16-bd01061272cc"},"outputs":[{"data":{"text/plain":["(torch.Size([16, 3]), torch.Size([16]))"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["X.shape, Y.shape # dataset"]},{"cell_type":"markdown","metadata":{"id":"N4sKUtAWaOSD"},"source":["Poi ridefiniamo i parametri (questa volta inseriamo il seed per rendere il tutto riproducibile)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EAA0_oigc13X"},"outputs":[],"source":["g = torch.Generator().manual_seed(2147483647) # for reproducibility\n","C = torch.randn((27, 2), generator=g)\n","W1 = torch.randn((6, 100), generator=g)\n","b1 = torch.randn(100, generator=g)\n","W2 = torch.randn((100, 27), generator=g)\n","b2 = torch.randn(27, generator=g)\n","parameters = [C, W1, b1, W2, b2] # lista con tutti i parametri"]},{"cell_type":"markdown","metadata":{"id":"kGvOg6w7acLJ"},"source":["Inseriamo un codice per poter contare tutti i parametri, in modo da poterci fare una idea delle dimensioni della rete"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":300,"status":"ok","timestamp":1719606381831,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"6sxXb9uUalPp","outputId":"cc67ebd8-0f78-4d51-ac3b-c67ceb9ba9bd"},"outputs":[{"data":{"text/plain":["3481"]},"execution_count":114,"metadata":{},"output_type":"execute_result"}],"source":["sum(p.nelement() for p in parameters) # numero di parametri totali"]},{"cell_type":"markdown","metadata":{"id":"Z669z-aKbDGv"},"source":["Definiamo i logits:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7dBasuu1dCw0"},"outputs":[],"source":["emb = C[X] # emb.shape = (16, 3, 2)\n","h = torch.tanh(emb.view(-1, 6) @ W1 + b1) # h.shape = (16, 100)\n","logits = h @ W2 + b2 # logits.shape = (16, 27)"]},{"cell_type":"markdown","metadata":{"id":"HbH7B9jAdEco"},"source":["<a name=\"s5.1\"></a>\n","### **5.1 - Calcolo della perdita: funzione `cross_entropy()`**\n","\n","Ora possiamo calcolare la perdita. Inizialmente l'abbiamo calcolata nel modo spiegato nella lezione precedente, ovvero:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1719600860320,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"xG3eT0bCbDku","outputId":"c44c00fa-1290-4bb2-c221-69cfb9d4c44c"},"outputs":[{"data":{"text/plain":["tensor(20.2417)"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["counts = logits.exp()\n","prob = counts / counts.sum(1, keepdim=True)\n","loss = -prob[torch.arange(16), Y].log().mean() # il 16 deriva dalla dimensione data dall'esempio\n","loss"]},{"cell_type":"markdown","metadata":{"id":"Pgq7GjvKdn4S"},"source":["Tuttavia, questo metodo di classificazione (ovvero di confrontare l'etichetta prevista con l'etichetta reale attraverso questo processo specifico) è già contenuto in una delle funzioni di `PyTorch`, che però la svolge in maniera molto più efficiente, non creando così tanti diversi tensori ad ogni iterazione del processo, effettuando invece le operazionì in maniera clusterizzata e molto più efficiente.\n","\n","Possiamo infatti testare e vedere che il risultato è esattamente lo stesso, e rimpiazzare quelle tre righe di codice con questa:\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1719600862003,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"FDqnefxbeyrT","outputId":"2b765c05-ddf7-4539-c605-4ed76c64a392"},"outputs":[{"data":{"text/plain":["tensor(20.2417)"]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["loss = F.cross_entropy(logits, Y)\n","loss"]},{"cell_type":"markdown","metadata":{"id":"fKrRXaZme5vc"},"source":["&nbsp;\n","\n","---\n","\n","Ci sono altre 2 ragioni per le quali è conveniente utilizzare la funzione `cross_entropy()`:\n","\n","1) Oltre a semplificare il forward pass, non creando tutti quei tensori ad ogni iterazione, semplifica di molto il backward pass. Per capire il motivo, torniamo a vedere come, nella prima lezione, dove abbiamo creato la libreria `micrograd`, abbiamo definito la funzione tangente hessiana, per poi calcolarne la derivata:\n","\n","```\n","    def tanh(self):\n","        x = self.data\n","        t = (math.exp(2*x) - 1)/(math.exp(2*x) + 1)\n","        out = Value(t, (self, ), 'tanh')\n","\n","        def _backward():\n","            self.grad += (1.0 - t**2) * out.grad\n","\n","        out._backward = _backward\n","\n","        return out\n","```\n","\n","Poiché abbiamo definito la funzione della tangente hessiana, durante la back propagation non vengono ricalcolate tutte le derivate, ma viene sostanzialmente  solamente effettuato il calcolo `1.0 - t**2`, che è molto più semplice a livello computazionale, visto che `t` è già stato calcolato nel forward pass.\n","\n","2) È meno suscettibile a problemi dati dalla variabilità numerica dei `logits`.\n","\n","Poiché i `counts` vengono calcolati elevando `e` alla potenza dei `logits`, per valori troppo alti il sistema non riusce più a calcolare la probabilità, perché i valori tendono ad infinito (superano la memoria disponibile per i `float`). Possiamo vederlo rapidamente con un esempio:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":372,"status":"ok","timestamp":1719600867832,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"pmkLk2FmhSTE","outputId":"1d855b94-7fe3-4283-9ff2-4c8320afc124"},"outputs":[{"data":{"text/plain":["tensor([0.0067, 0.0498, 1.0000,    inf])"]},"execution_count":40,"metadata":{},"output_type":"execute_result"}],"source":["logits_test = torch.tensor([-5, -3, 0, 100]) # 100 è il valore troppo alto\n","counts_test = logits_test.exp()\n","counts_test"]},{"cell_type":"markdown","metadata":{"id":"RMasDqWeh_vG"},"source":["La funzione di `PyTorch` gestisce questo problema, nel seguente modo: poiché per via della natura dell'operazione i risultati possono essere trasposti senza cambiare il risultato, `PyTorch` individua il valore più alto tra i `logits` e lo sottrae a tutti i valori, effettuando una operazione equivalente al codice sotto:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":292,"status":"ok","timestamp":1719600868538,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"R2lT10Lciizm","outputId":"d9f7e3c3-1a3d-4779-a07e-e4622f286661"},"outputs":[{"data":{"text/plain":["tensor([0.0000e+00, 1.4013e-45, 3.7835e-44, 1.0000e+00])"]},"execution_count":41,"metadata":{},"output_type":"execute_result"}],"source":["logits_test = torch.tensor([-5, -3, 0, 100]) - 100 # sottrae il valore più alto\n","counts_test = logits_test.exp()\n","counts_test"]},{"cell_type":"markdown","metadata":{"id":"kWGd05byiqSM"},"source":["Questo passaggio viene fatto in automatico dalla funzione `cross_entropy()`.\n","\n","---\n","\n","&nbsp;"]},{"cell_type":"markdown","metadata":{},"source":["<a name=\"s6\"></a>\n","## **6 - Addestramento**\n","\n","Adesso possiamo scrivere tutto il processo, comprendente il forward pass, azzeramento dei gradienti e backward pass per il calcolo gradienti e l'ottimizzazione dei pesi, facendolo ripetere ciclicamente, questo dopo aver impostato che i parametri debbano avere i gradienti (di default non lo hanno)."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"coNBea1sc6tm"},"outputs":[],"source":["for p in parameters:\n","  p.requires_grad = True"]},{"cell_type":"markdown","metadata":{},"source":["Effettuiamo l'addestranento:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2062,"status":"ok","timestamp":1719602002956,"user":{"displayName":"Josho 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_ in range(1000):\n","\n","  # forward pass\n","  emb = C[X] # (16, 3, 2)\n","  h = torch.tanh(emb.view(-1, 6) @ W1 + b1) # (16, 100)\n","  logits = h @ W2 + b2 # (16, 27)\n","  loss = F.cross_entropy(logits, Y)\n","  print(loss.item())\n","\n","  # backward pass\n","  for p in parameters:\n","    p.grad = None\n","  loss.backward()\n","\n","  # update\n","  for p in parameters:\n","    p.data += -0.1 * p.grad\n","\n","# print(loss.item()) # per vedere solamente il valore finale della perdita commentare l'altro print e attiare questo"]},{"cell_type":"markdown","metadata":{"id":"UQUjqOeVNkKn"},"source":["Con un elevato numero di cicli l'errore diventa bassissimo, questo però accade perché stiamo utilizzando pochissimi dati rispetto al numero di parametri, perciò sostanzialmente si sta verificando l'overfitting, ottenendo ottime previsioni e una perdita bassissima.\n","\n","Anche in questo caso, il modello non è in grado di portare la perdita a zero, infatti possiamo controllare, osservando i logits (che rappresentano la distribuzione di probabilità che viene assegnata ad ogni indice), ed in particolare al valore massimo di ciascuna riga (ovvero, il valore che il modello utilizzerebbe per la previsione), confrontando con i valori effettivi di `Y` (che sono le etichette), ci saranno alcune previsioni errate:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":291,"status":"ok","timestamp":1719602015048,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"8nuyELaIOeU0","outputId":"2f3ffd69-f08f-4daa-9d9e-5f5994f8a83b"},"outputs":[{"data":{"text/plain":["torch.return_types.max(\n","values=tensor([16.1525, 20.2416, 22.4765, 22.2766, 16.5530, 16.1525, 18.2706, 19.3840,\n","        16.5037, 18.5166, 17.7439, 22.4792, 16.1525, 17.7114, 20.6718, 24.1955],\n","       grad_fn=<MaxBackward0>),\n","indices=tensor([ 1, 13, 13,  1,  0,  1, 12,  9, 22,  9,  1,  0,  1, 22,  1,  0]))"]},"execution_count":76,"metadata":{},"output_type":"execute_result"}],"source":["logits.max(1) # la funzione max ci fornirà anche gli indici, oltre alle previsioni, comodo per confrontare con Y"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":361,"status":"ok","timestamp":1719601340688,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"jgkncn1yOg1i","outputId":"894561ac-dc5f-446c-ff1e-24652126e89d"},"outputs":[{"data":{"text/plain":["tensor([ 5, 13, 13,  1,  0, 15, 12,  9, 22,  9,  1,  0,  1, 22,  1,  0])"]},"execution_count":47,"metadata":{},"output_type":"execute_result"}],"source":["Y"]},{"cell_type":"markdown","metadata":{"id":"p5d5ooxAQE44"},"source":["Come previsto, possiamo infatti vedere che alcune previsioni sono errate.\n","\n","Questo accade perché il modello non ha la possibilità di prevedere cosa inserire in determinate posizioni (come, ad esempio, la `e`, la `o` e la `a` all'inizio, dopo `...` a causa del fatto che gli unici tre nomi presenti nel dataset sono `emma`, `olivia` e `ava`), ragion per cui non riesce ad overfittare completamente ai dati.\n","\n","Possiamo ora procedere con il considerare tutti i dati:\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bhDEASrsSZYc"},"outputs":[],"source":["# build the dataset\n","block_size = 3 # context length: how many characters do we take to predict the next one?\n","\n","X, Y = [], []\n","for w in words[:]:\n","\n","  # print(w)\n","  context = [0] * block_size\n","  for ch in w + '.':\n","    ix = stoi[ch]\n","    X.append(context)\n","    Y.append(ix)\n","    # print(''.join(itos[i] for i in context), '--->', itos[ix])\n","    context = context[1:] + [ix]\n","\n","X = torch.tensor(X)\n","Y = torch.tensor(Y)"]},{"cell_type":"markdown","metadata":{"id":"upENvS2eSsax"},"source":["Vediamo ora quanti esempi ci sono in totale:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":290,"status":"ok","timestamp":1719602486314,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"nZD9vYi-S5AO","outputId":"8bfb423b-6c08-46c3-a172-b1538e4f8a5f"},"outputs":[{"data":{"text/plain":["(torch.Size([228146, 3]), torch.int64, torch.Size([228146]), torch.int64)"]},"execution_count":78,"metadata":{},"output_type":"execute_result"}],"source":["X.shape, X.dtype, Y.shape, Y.dtype"]},{"cell_type":"markdown","metadata":{"id":"Az7v6TEoS9Nf"},"source":["Aggiorniamo i parametri della rete"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wPm2LUiNS857"},"outputs":[],"source":["g = torch.Generator().manual_seed(2147483647) # for reproducibility\n","C = torch.randn((27, 2), generator=g)\n","W1 = torch.randn((6, 100), generator=g)\n","b1 = torch.randn(100, generator=g)\n","W2 = torch.randn((100, 27), generator=g)\n","b2 = torch.randn(27, generator=g)\n","parameters = [C, W1, b1, W2, b2] # lista con tutti i parametri"]},{"cell_type":"markdown","metadata":{"id":"IbZQrAfjTowx"},"source":["Facciamo in modo che i parametri ammettano i gradienti:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"h0YqsRhfTofO"},"outputs":[],"source":["for p in parameters:\n","  p.requires_grad = True"]},{"cell_type":"markdown","metadata":{"id":"qXPXziAcTRAk"},"source":["Addestriamo la rete:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9262,"status":"ok","timestamp":1719602714135,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"JdQ_nTMETYi4","outputId":"b6e1edc2-34cc-477c-9071-83475cdfb720"},"outputs":[{"name":"stdout","output_type":"stream","text":["19.505226135253906\n","17.08449363708496\n","15.776531219482422\n","14.833340644836426\n","14.002603530883789\n","13.253260612487793\n","12.57991886138916\n","11.983101844787598\n","11.47049331665039\n","11.051856994628906\n"]}],"source":["for _ in range(10):\n","\n","  # forward pass\n","  emb = C[X]\n","  h = torch.tanh(emb.view(-1, 6) @ W1 + b1)\n","  logits = h @ W2 + b2\n","  loss = F.cross_entropy(logits, Y)\n","  print(loss.item())\n","\n","  # backward pass\n","  for p in parameters:\n","    p.grad = None\n","  loss.backward()\n","\n","  # update\n","  for p in parameters:\n","    p.data += -0.1 * p.grad # '-0.1' è il learning rate (scelto arbitrariamente per ora)\n","\n","# print(loss.item())"]},{"cell_type":"markdown","metadata":{"id":"Jpt_HRRJT0vi"},"source":["<a name=\"s6.1\"></a>\n","### **6.1 - Batching**\n","\n","Ora che abbiamo un dataset di dimensioni maggiori, possiamo notare che la rete impiega più tempo per addestrarsi. Per risolvere questo problema (specialmente in vista di modelli e datasets di maggiori dimensioni), si separano i dati gruppi di piccoli dimensioni (`batches`), e ad ogni iterazione del modello esso si addestra con un gruppo diverso di dati (scelto randomicamente). In questo modo, il modello impiega più iterazioni per ridurre la perdita, ma guadagna nettamente in efficienza, perciò e quasi sempre conveniente farlo.\n","\n","Procediamo quindi ad includere questo aspetto, generando un minibatch di dimensione `32` (quindi il modello si addestrerà su 32 esempi ad ogni iterazione, questo è un parametro che possiamo scegliere noi arbitrariamente) che seleziona in maniera randomica `32` esempi, compresi tra `0` e il numero di esempi `X` (che, nel nostro caso, sono `228146`):"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":709,"status":"ok","timestamp":1719604022299,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"3bE3gOTIU1Ml","outputId":"1e38c42f-70f6-4e40-d392-cd0e556007e4"},"outputs":[{"name":"stdout","output_type":"stream","text":["2.5769057273864746\n","2.2340340614318848\n","2.4338855743408203\n","2.188222646713257\n","2.4399969577789307\n","2.3970515727996826\n","2.4423015117645264\n","2.628464460372925\n","2.47308349609375\n","2.6569740772247314\n","2.4103314876556396\n","2.5125811100006104\n","2.412407636642456\n","2.707550525665283\n","2.3109869956970215\n","2.3681082725524902\n","2.676372766494751\n","2.400599241256714\n","2.202120065689087\n","2.4403724670410156\n","3.027336597442627\n","2.3254525661468506\n","2.3339250087738037\n","2.3267924785614014\n","2.854828357696533\n","2.373764753341675\n","2.292238235473633\n","2.1740784645080566\n","2.0634000301361084\n","2.323490858078003\n","2.590900182723999\n","2.393386125564575\n","2.3808634281158447\n","2.372981071472168\n","2.5698814392089844\n","2.767880439758301\n","2.3848769664764404\n","2.596428394317627\n","2.3928487300872803\n","2.5163044929504395\n","2.343933343887329\n","2.1646528244018555\n","2.447134494781494\n","2.552027463912964\n","2.3680496215820312\n","2.7187769412994385\n","2.351623296737671\n","2.321320056915283\n","2.3444583415985107\n","2.660668134689331\n","2.38643741607666\n","2.1805121898651123\n","2.544351577758789\n","2.6175081729888916\n","2.8946406841278076\n","2.702643632888794\n","2.5546164512634277\n","2.254446268081665\n","2.326967239379883\n","2.1140286922454834\n","2.202610731124878\n","2.417757272720337\n","2.6916770935058594\n","2.1934802532196045\n","2.5468592643737793\n","2.740950345993042\n","2.772111177444458\n","2.5107336044311523\n","2.7134761810302734\n","2.5407557487487793\n","2.413339138031006\n","2.584388017654419\n","2.6448745727539062\n","2.3979384899139404\n","2.7026491165161133\n","2.102816343307495\n","2.522709608078003\n","2.2723681926727295\n","2.921614646911621\n","2.686915397644043\n","2.296511650085449\n","2.8028290271759033\n","2.1579201221466064\n","2.5940349102020264\n","2.6006386280059814\n","2.404970407485962\n","2.3564491271972656\n","2.6574509143829346\n","2.615196704864502\n","2.643364906311035\n","2.054124593734741\n","2.46855092048645\n","2.6050901412963867\n","2.046846389770508\n","2.6494035720825195\n","2.5148873329162598\n","2.857612371444702\n","2.3907153606414795\n","1.9339743852615356\n","1.9333288669586182\n"]}],"source":["for _ in range(100):\n","\n","  # costruzione del minibatch\n","  ix = torch.randint(0, X.shape[0], (32,)) # il \"(32,)\" è scritto così perché torch.randint accetta solamente tuple come dimensione del batch\n","\n","  # forward pass\n","  emb = C[X[ix]] # verranno selezionati solo gli elementi di X presenti del batch\n","  h = torch.tanh(emb.view(-1, 6) @ W1 + b1)\n","  logits = h @ W2 + b2\n","  loss = F.cross_entropy(logits, Y[ix]) # verranno selezionati solo gli elementi di Y presenti del batch\n","  print(loss.item())\n","\n","  # backward pass\n","  for p in parameters:\n","    p.grad = None\n","  loss.backward()\n","\n","  # aggiornamento dei parametri\n","  for p in parameters:\n","    p.data += -0.1 * p.grad\n","\n","#print(loss.item())"]},{"cell_type":"markdown","metadata":{"id":"rBsSmfn7WwRV"},"source":["Poiché il modello si addestra con dati diversi ad ogni iterazione, la qualità del gradiente non è affidabile come quello che si avrebbe considerando il dataset per intero, perciò la riduzione della funzione di perdita avverrà con rumore e ci saranno quindi degli alti e bassi.\n","\n","Il modello però è drasticamente più rapido nell'addestramento (la differenza di efficienza diventa sempre più visibile man mano che il modello e il dataset aumentano di dimensioni), perciò nella pratica funziona molto bene.\n","\n","Dopo essere arrivati ad un livello di perdita ragionevolmente basso, si può effettuare una unica iterazione su tutti i dati (per individuare il valore complessivo della perdita, e non solamente su un batch)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":711,"status":"ok","timestamp":1719604027019,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"jrukyE3fYT0D","outputId":"ecbc2732-54ea-4778-bfbf-a50d920f90e5"},"outputs":[{"name":"stdout","output_type":"stream","text":["2.498107671737671\n"]}],"source":["# forward pass (singola iterazione, su tutto il dataset)\n","emb = C[X]\n","h = torch.tanh(emb.view(-1, 6) @ W1 + b1)\n","logits = h @ W2 + b2\n","loss = F.cross_entropy(logits, Y)\n","print(loss.item())"]},{"cell_type":"markdown","metadata":{"id":"idhXAkc3aBWM"},"source":["<a name=\"s6.2\"></a>\n","### **6.2 - Learning rate**\n","\n","Per learning rate si intende il valore che viene moltiplicato al gradiente ad ogni iterazione del processo di addestramento. Decide quindi quanto ci si muove rapidamente verso il gradiente ad ogni iterazione.\n","\n","Fino ad ora abbiamo addestrato la rete con un learning rate scelto in maniera arbitraria, sperando andasse bene. Un valore troppo basso impiegherebbe troppe iterazioni per convergere, mentre un valore troppo alto porterebbe a non far convergere affatto, non riuscendo ad ottimizzare il modello.\n","\n","---\n","\n","*Per avere una spiegazione più dettagliata dei concetti appena descritti si rimanda al capitolo 5 della lezione 1: \"**Demistificare le reti neurali e la back propagation**\".*\n","\n","---\n","\n","Possiamo analizzare la discesa della perdita, al variare del learning rate, in modo da comprendere qual è il valore ottimale per questo caso specifico.\n","\n","Definiamo quindi una lista di valori da utilizzare come lerning rate."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8foQ5funcUKG"},"outputs":[],"source":["lre = torch.linspace(-3, 0, 1000) # 1000 valori scelti in un intervallo che cresce linearmente tra -3 e 0\n","lrs = 10**lre # il valore che ci interessa parte da 0,001 fino ad 1, con l'esponente che cresce in maniera lineare (ovvero cresce più rapidamente man mano che ci si avvicina all'1)"]},{"cell_type":"markdown","metadata":{"id":"XQom0ueHd9s9"},"source":["Possiamo ora riaddestrare la rete per 1000 iterazioni, cambiando ad ogni iterazione il learning rate (partendo dal valore più basso).\n","\n","Terremo traccia della riduzione della perdita, in modo da poter poi tracciare su un grafico e individuare il tasso ottimale.\n","\n","Resettiamo i parametri ancora una volta"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"v_ucbueYeifN"},"outputs":[],"source":["g = torch.Generator().manual_seed(2147483647) # for reproducibility\n","C = torch.randn((27, 2), generator=g)\n","W1 = torch.randn((6, 100), generator=g)\n","b1 = torch.randn(100, generator=g)\n","W2 = torch.randn((100, 27), generator=g)\n","b2 = torch.randn(27, generator=g)\n","parameters = [C, W1, b1, W2, b2] # lista con tutti i parametri"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gXqaiWmZg4q3"},"outputs":[],"source":["for p in parameters:\n","  p.requires_grad = True"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"executionInfo":{"elapsed":1566,"status":"ok","timestamp":1719606460607,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"kzcIbX4Mei0j","outputId":"b476f0d5-fced-4629-8365-dec274cb6322"},"outputs":[{"data":{"text/plain":["[<matplotlib.lines.Line2D at 0x78e11a183190>]"]},"execution_count":122,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# liste per registrare il learning rate utilizzato e la perdita per quella iterazione\n","lri = [] # inizializziamo il learning rate utilizzato con una lista vuota\n","lossi = [] # inizializziamo la perdita con una lista vuota\n","\n","for i in range(1000):\n","\n","  # minibatch construct\n","  ix = torch.randint(0, X.shape[0], (32,)) # il \"(32,)\" è scritto così perché torch.randint accetta solamente tuple come dimensione del batch\n","\n","  # forward pass\n","  emb = C[X[ix]] # verranno selezionati solo gli elementi di X presenti del batch\n","  h = torch.tanh(emb.view(-1, 6) @ W1 + b1)\n","  logits = h @ W2 + b2\n","  loss = F.cross_entropy(logits, Y[ix]) # verranno selezionati solo gli elementi di Y presenti del batch\n","  #print(loss.item())\n","\n","  # backward pass\n","  for p in parameters:\n","    p.grad = None\n","  loss.backward()\n","\n","  # update\n","  lr = lrs[i]\n","  for p in parameters:\n","    p.data += -lr * p.grad\n","\n","  # track stats\n","  lri.append(lr) # aggiungiamo il learning rate appena utilizzato\n","  lossi.append(loss.item()) # aggiungiamo la perdita a fine iterazione\n","\n","plt.plot(lri, lossi)"]},{"cell_type":"markdown","metadata":{"id":"NKdMn-ZgiQlv"},"source":["Dalla curva si può vedere che, in questo caso, il learning rate più adatto è intorno a `0.1`, in quanto, per valori torppo piccoli c'è poca variazione di punto in punto, mentre per valori superiori la curva inizia a diventare instabile.\n","\n","Avremmo potuto osservarlo anche tracciando l'esponente (ovvero `lre`, al posto di `lrs`), dopo aver nuovamente resettato i parametri:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"executionInfo":{"elapsed":1230,"status":"ok","timestamp":1719607001516,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"Wz_YJ5ikjq5i","outputId":"4ac30c60-fcd8-4c95-8a71-0684b0df3ac7"},"outputs":[{"data":{"text/plain":["[<matplotlib.lines.Line2D at 0x78e11aa6dde0>]"]},"execution_count":125,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# liste per registrare il learning rate utilizzato e la perdita per quella iterazione\n","lri = [] # inizializziamo il learning rate utilizzato con una lista vuota\n","lossi = [] # inizializziamo la perdita con una lista vuota\n","\n","for i in range(1000):\n","\n","  # minibatch construct\n","  ix = torch.randint(0, X.shape[0], (32,)) # il \"(32,)\" è scritto così perché torch.randint accetta solamente tuple come dimensione del batch\n","\n","  # forward pass\n","  emb = C[X[ix]] # verranno selezionati solo gli elementi di X presenti del batch\n","  h = torch.tanh(emb.view(-1, 6) @ W1 + b1)\n","  logits = h @ W2 + b2\n","  loss = F.cross_entropy(logits, Y[ix]) # verranno selezionati solo gli elementi di Y presenti del batch\n","  #print(loss.item())\n","\n","  # backward pass\n","  for p in parameters:\n","    p.grad = None\n","  loss.backward()\n","\n","  # update\n","  lr = lrs[i]\n","  for p in parameters:\n","    p.data += -lr * p.grad\n","\n","  # track stats\n","  lri.append(lre[i]) # aggiungiamo il learning rate appena utilizzato\n","  lossi.append(loss.item()) # aggiungiamo la perdita a fine iterazione\n","\n","plt.plot(lri, lossi)"]},{"cell_type":"markdown","metadata":{"id":"IQi4ptzTkJKn"},"source":["Possiamo vedere che gli esponenti migliori sono intorno al `-1.0`, che corrispondono ad un rate di `0.1`."]},{"cell_type":"markdown","metadata":{"id":"OssOe6oLk7d_"},"source":["<a name=\"s6.2.1\"></a>\n","#### **6.2.1 - Learning rate decay**\n","\n","Dopo aver raggiunto un buon livello di perdita con il tasso \"ottimale\" che abbiamo individuato precedentemente, è possibile ridurre ancora la perdita, riducendo il valore del learning rate ed effettuando qualche ulteriore ciclo di addestramento. \n","\n","Questo processo permette di ottenere utleriori miglioramenti, anche dopo che l'addestramento con il tasso precedente ha raggiunto uno stallo.\n","\n","Possiamo quindi modificare l'addestramento, per fare in modo che un certo numero di cicli venga fatto con il tasso pari a `0.1` e successivamente vengano effettuati altri cicli con un tasso pari a `0.01`"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2KBDWd1Olzf0"},"outputs":[],"source":["for i in range(20000):\n","\n","  # minibatch construct\n","  ix = torch.randint(0, X.shape[0], (32,)) # il \"(32,)\" è scritto così perché torch.randint accetta solamente tuple come dimensione del batch\n","\n","  # forward pass\n","  emb = C[X[ix]] # verranno selezionati solo gli elementi di X presenti del batch\n","  h = torch.tanh(emb.view(-1, 6) @ W1 + b1)\n","  logits = h @ W2 + b2\n","  loss = F.cross_entropy(logits, Y[ix]) # verranno selezionati solo gli elementi di Y presenti del batch\n","  #print(loss.item())\n","\n","  # backward pass\n","  for p in parameters:\n","    p.grad = None\n","  loss.backward()\n","\n","  # update\n","  lr = 0.1 if i < 10000 else 0.01\n","  for p in parameters:\n","    p.data += -lr * p.grad\n","\n"]},{"cell_type":"markdown","metadata":{"id":"2BcIO8hfmDQB"},"source":["<a name=\"s6.3\"></a>\n","### **6.3 - Scomposizione del dataset in Train, Validation e Test**\n","\n","Cerchiamo ora di non far accadere il fenomeno dell'overfitting. Addestrare il modello su tutti i dati, e cercando di ridurre la perdita nell'indovinarli, porta il modello a memorizzare tutti i pattern possibili e può portare ad una perdita bassissima, tuttavia, il modello addestrato in questo modo potrebbe non essere capace di prevedere accuratamente dati nuovi, ovvero non presenti sul dataset su cui si è addestrato. Questo fenomeno si verifica tanto più quanto il modello è di grandi dimensioni, in quanto man mano che diventa più potente diventa anche più capace comprendere e memorizzare tutte le relazioni tra i dati, al punto da poterli replicare. Si vedrebbe, infatti, che la perdita che si avrebbe nell'indovinare parole all'interno del dataset di addestramento sarebbe molto bassa, mentre, per parole non presenti nel dataset, molto elevata.\n","\n","Per risolvere questo problema si può dividere il dataset in 3 split differenti:\n","\n","1. **Train**: comprende gran parte dei dati, e viene utilizzato per addestrare il modello, che avrà la possibilità diapprendere osservando le etichette. Serve a migliorare i parametri del modello (ad esempio il learning rate).   \n","\n","2. **Dev/Validation**: è uno split il cui scopo è dare la possibilità di effettuare modifiche agli iperparametri della rete (dimensione dell'hidden layer, dimensione dell'embedding, ecc.).\n","\n","3. **Test**: split utilizzato per testare le performance del modello dopo che sono stati raggiunti risultati soddisfacenti con i primi due split. La prova con questo split deve essere fatta pochissime volte, in quanto ogni volta che viene valutato il modello su questo split e si impara qualcosa da esso, si sta iniziando sostanzialmente utilizzando questo split come addestramento, rischiando di far overfittare il modello ad esso.\n","\n","Generalmente una buona suddivisione può essere 80%, 10%, 10%.\n","\n","Definiamo quindi la funzione che crea i tre differenti datasets:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1017,"status":"ok","timestamp":1719609253866,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"7NoQDp2AzzF7","outputId":"2d2cfc3c-ee70-4f1d-8c0a-f18d96567a88"},"outputs":[{"name":"stdout","output_type":"stream","text":["torch.Size([182580, 3]) torch.Size([182580])\n","torch.Size([22740, 3]) torch.Size([22740])\n","torch.Size([22826, 3]) torch.Size([22826])\n","---\n","Train size = 25626\n","Dev size = 3203\n","Test size = 3204\n"]}],"source":["# build the dataset\n","block_size = 3 # context length: how many characters do we take to predict the next one?\n","\n","# definizione della funzione, che verrà chiamata di seguito per definire i dataset di train dev e test\n","def build_dataset(words):\n","  X, Y = [], []\n","  for w in words:\n","\n","    # print(w)\n","    context = [0] * block_size\n","    for ch in w + '.':\n","      ix = stoi[ch]\n","      X.append(context)\n","      Y.append(ix)\n","      # print(''.join(itos[i] for i in context), '--->', itos[ix])\n","      context = context[1:] + [ix] # crop and append\n","\n","  X = torch.tensor(X)\n","  Y = torch.tensor(Y)\n","  print(X.shape, Y.shape) # dimensioni di X e Y ad ognuna delle 3 iterazioni (train, dev, test)\n","  return X, Y\n","\n","import random\n","random.seed(42)\n","random.shuffle(words) # mischia tutte le parole\n","n1 = int(0.8*len(words)) # 80% delle parole\n","n2 = int(0.9*len(words)) # 90% delle parole\n","\n","Xtr, Ytr = build_dataset(words[:n1]) # train da 0 a 80%\n","Xdev, Ydev = build_dataset(words[n1:n2]) # dev da 80% a 90%\n","Xte, Yte = build_dataset(words[n2:]) # test da 90% a 100%\n","\n","print(\"---\")\n","\n","print(f\"Train size = {len(words[:n1])}\")\n","print(f\"Dev size = {len(words[n1:n2])}\")\n","print(f\"Test size = {len(words[n2:])}\")"]},{"cell_type":"markdown","metadata":{"id":"dQiPRtaIqRNm"},"source":["I primi 3 print forniscono la quantità di esempi contenuti in ciascuno dei tre datasets, i secondi 3 print indicano il numero di parole presenti in ogni dataset.\n","\n","Adesso possiamo resettare nuovamente la rete, e addestrarla solamente sui dati di train:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RncqzwTXtfvT"},"outputs":[],"source":["g = torch.Generator().manual_seed(2147483647) # for reproducibility\n","C = torch.randn((27, 2), generator=g)\n","W1 = torch.randn((6, 100), generator=g)\n","b1 = torch.randn(100, generator=g)\n","W2 = torch.randn((100, 27), generator=g)\n","b2 = torch.randn(27, generator=g)\n","parameters = [C, W1, b1, W2, b2] # lista con tutti i parametri"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PNMJMc2ftjZH"},"outputs":[],"source":["for p in parameters:\n","  p.requires_grad = True"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nFWpAj_9c9Yr"},"outputs":[],"source":["lre = torch.linspace(-3, 0, 1000)\n","lrs = 10**lre"]},{"cell_type":"markdown","metadata":{"id":"XTAf9W7zwdGr"},"source":["Aggiungiamo un blocco per poter effettuare le analisi in seguito:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"t2huuyTudBlF"},"outputs":[],"source":["lri = []\n","lossi = []\n","stepi = []"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":76323,"status":"ok","timestamp":1719610820377,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"jiKD-PPOdClh","outputId":"ebbcdad4-8f38-479c-de78-7c005f56cf84"},"outputs":[{"name":"stdout","output_type":"stream","text":["2.3384315967559814\n"]}],"source":["for i in range(100000):\n","\n","  # minibatch construct\n","  ix = torch.randint(0, Xtr.shape[0], (32,))\n","\n","  # forward pass\n","  emb = C[Xtr[ix]]\n","  h = torch.tanh(emb.view(-1, 6) @ W1 + b1)\n","  logits = h @ W2 + b2\n","  loss = F.cross_entropy(logits, Ytr[ix])\n","  #print(loss.item())\n","\n","  # backward pass\n","  for p in parameters:\n","    p.grad = None\n","  loss.backward()\n","\n","  # update\n","  #lr = lrs[i]\n","  lr = 0.1 if i < 50000 else 0.01\n","  for p in parameters:\n","    p.data += -lr * p.grad\n","\n","  # track stats\n","  #lri.append(lre[i])\n","  stepi.append(i)\n","  lossi.append(loss.log10().item()) # possiamo utilizzare il logaritmo per vedere meglio i risultati graficamente\n","\n","#print(loss.item())"]},{"cell_type":"markdown","metadata":{"id":"q1c5xyTxxTjG"},"source":["Osserviamo graficamente la riduzione della perdita, vedendo il logaritmo per fare in modo che la parte iniziale che è molto alta venga schiacciata, permettendo di vedere meglio il resto:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"executionInfo":{"elapsed":489,"status":"ok","timestamp":1719610820855,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"ZBAYXZ_rdGIG","outputId":"69646513-58b3-45dc-98f0-fd4ce07887d7"},"outputs":[{"data":{"text/plain":["[<matplotlib.lines.Line2D at 0x78e11a3f6aa0>]"]},"execution_count":163,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.plot(stepi, lossi)"]},{"cell_type":"markdown","metadata":{"id":"Jp0s7lWxt9RW"},"source":["Possiamo vedere che la perdita ha del rumore, per via dell'utilizzo dei mini batches.\n","\n","Vediamo la perdita complessiva su tutto il dataset di train (rispetto a considerare solamente i mini batches)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":699,"status":"ok","timestamp":1719610883027,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"ZlhYinNgdIVF","outputId":"2b159809-6af7-4e1e-d200-1c0c4695355b"},"outputs":[{"data":{"text/plain":["tensor(2.2913, grad_fn=<NllLossBackward0>)"]},"execution_count":166,"metadata":{},"output_type":"execute_result"}],"source":["# training loss\n","emb = C[Xtr]\n","h = torch.tanh(emb.view(-1, 6) @ W1 + b1)\n","logits = h @ W2 + b2\n","loss = F.cross_entropy(logits, Ytr)\n","loss"]},{"cell_type":"markdown","metadata":{"id":"NIb-JU9lyrZG"},"source":["Ora passiamo ad effettuare la validazione, sul dataset di dev:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":303,"status":"ok","timestamp":1719610915782,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"oVeNg8cLuGlM","outputId":"f97b2a1e-1a28-40ca-f02b-e3f8fcb91659"},"outputs":[{"data":{"text/plain":["tensor(2.3062, grad_fn=<NllLossBackward0>)"]},"execution_count":167,"metadata":{},"output_type":"execute_result"}],"source":["# dev loss\n","emb = C[Xdev] # (32, 3, 2)\n","h = torch.tanh(emb.view(-1, 6) @ W1 + b1) # (32, 100)\n","logits = h @ W2 + b2 # (32, 27)\n","loss = F.cross_entropy(logits, Ydev)\n","loss"]},{"cell_type":"markdown","metadata":{"id":"T_L9CrhLu5As"},"source":["Possiamo vedere che, sebbene la validazione stia venendo fatta sui dati di dev, che la rete non ha mai visto, la differenza tra gli errori è marginale. **Questo vuol dire che il modello non riesce a fare overfitting**, ovvero non è abbastanza potente da apprendere i pattern dei dati. **Ciò ci indica che il modello è ancora migliorabile**, perché se non riesce a causare overfitting vuol dire che un modello che invece ci riesce potrebbe ottenere una perdita ancora più bassa.\n","\n","Questo è una dimostrazione del motivo per il quale si effettua una prima validazione con i dati di dev. Questa informazione ci fa capire che potremmo modificare gli iperparametri per rendere la rete più potente (ad esempio, aumentando i neuroni dell'hidden layer, oppure aumentando le dimensioni dell'embedding layer, ad esempio da 2 a 3).\n","\n","Poiché al momento stiamo utilizzando l'embedding layer a due dimensioni, possiamo effettivamente rappresentarlo, per vedere, a seguito dell'addestramento, come sono stati mappati i vari caratteri."]},{"cell_type":"markdown","metadata":{},"source":["<a name=\"s6.3.1\"></a>\n","#### **6.3.1 - Grafico della matrice di embedding post addestramento**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":676},"executionInfo":{"elapsed":885,"status":"ok","timestamp":1719610998186,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"CCWLYP9GdPP4","outputId":"98c63cbc-2751-4947-b3aa-4d9bc5bd6cb3"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 800x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# visualize dimensions 0 and 1 of the embedding matrix C for all characters\n","plt.figure(figsize=(8,8))\n","plt.scatter(C[:,0].data, C[:,1].data, s=200)\n","for i in range(C.shape[0]):\n","    plt.text(C[i,0].item(), C[i,1].item(), itos[i], ha=\"center\", va=\"center\", color='white')\n","plt.grid('minor')"]},{"cell_type":"markdown","metadata":{"id":"aBGWclpqzZ5z"},"source":["Il grafico mostra risultati interessanti. Il modello ha imparato a separare i caratteri, clusterizzandoli. Ha imparato che le vocali ed altri caratteri particolari vanno trattate in maniera diversa dalle altre lettere. Alcune vocali sono vicine tra loro, perciò il modello le considera interscambiabili.\n","\n","Ora proviamo a modificare gli iperparametri e a riaddestrare la rete, per vedere se migliora l'accuratezza e il modello riesce ad imparare meglio dai dati."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"n_4vkPCP09QR"},"outputs":[],"source":["g = torch.Generator().manual_seed(2147483647) # for reproducibility\n","C = torch.randn((27, 10), generator=g) # embedding in un vettore di 10 dimensioni per ogni parola (rispetto a prima che ne erano 2)\n","W1 = torch.randn((30, 200), generator=g) # 3*10 input (il 3 è dato dalle lettere di contesto), 200 output\n","b1 = torch.randn(200, generator=g) # hidden layer da 200 neuroni\n","W2 = torch.randn((200, 27), generator=g)\n","b2 = torch.randn(27, generator=g)\n","parameters = [C, W1, b1, W2, b2] # lista con tutti i parametri"]},{"cell_type":"markdown","metadata":{"id":"DUMIrR-A1rE9"},"source":["Vedaiamo quanti parametri ha ora la rete:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":503,"status":"ok","timestamp":1719611960685,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"5dJPrUiB1o6K","outputId":"4a924468-4a25-493c-aa13-29f4af71a51e"},"outputs":[{"data":{"text/plain":["11897"]},"execution_count":179,"metadata":{},"output_type":"execute_result"}],"source":["sum(p.nelement() for p in parameters) # numero di parametri totali"]},{"cell_type":"markdown","metadata":{"id":"9V9y0LMd12cp"},"source":["Sono aumentati molto rispetto a prima (che ne erano poco più di 3000)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nbc05WZJ187p"},"outputs":[],"source":["for p in parameters:\n","  p.requires_grad = True"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fm0_wj6x1_af"},"outputs":[],"source":["lre = torch.linspace(-3, 0, 1000)\n","lrs = 10**lre"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Pik8YQ1Y2Dx2"},"outputs":[],"source":["lri = []\n","lossi = []\n","stepi = []"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LMKCHrbs2Edp"},"outputs":[],"source":["for i in range(100000):\n","\n","  # minibatch construct\n","  ix = torch.randint(0, Xtr.shape[0], (32,))\n","\n","  # forward pass\n","  emb = C[Xtr[ix]]\n","  h = torch.tanh(emb.view(-1, 30) @ W1 + b1) # 30 per via delle dimensioni dell'embedding layer (3*10)\n","  logits = h @ W2 + b2\n","  loss = F.cross_entropy(logits, Ytr[ix])\n","  #print(loss.item())\n","\n","  # backward pass\n","  for p in parameters:\n","    p.grad = None\n","  loss.backward()\n","\n","  # update\n","  #lr = lrs[i]\n","  lr = 0.1 if i < 50000 else 0.01\n","  for p in parameters:\n","    p.data += -lr * p.grad\n","\n","  # track stats\n","  #lri.append(lre[i])\n","  stepi.append(i)\n","  lossi.append(loss.log10().item()) # possiamo utilizzare il logaritmo per vedere meglio i risultati graficamente\n","\n","#print(loss.item())"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":748,"status":"ok","timestamp":1719612053788,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"hUO7iZL323OQ","outputId":"0ceb0189-1817-4e31-86ef-a6609a3daf90"},"outputs":[{"data":{"text/plain":["tensor(2.1680, grad_fn=<NllLossBackward0>)"]},"execution_count":184,"metadata":{},"output_type":"execute_result"}],"source":["# training loss\n","emb = C[Xtr]\n","h = torch.tanh(emb.view(-1, 30) @ W1 + b1)\n","logits = h @ W2 + b2\n","loss = F.cross_entropy(logits, Ytr)\n","loss"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1719612053789,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"BYIs6dDz26iS","outputId":"ac7cc916-21ed-4bf6-aea9-b687baf2dfdb"},"outputs":[{"data":{"text/plain":["tensor(2.2041, grad_fn=<NllLossBackward0>)"]},"execution_count":185,"metadata":{},"output_type":"execute_result"}],"source":["# dev loss\n","emb = C[Xdev]\n","h = torch.tanh(emb.view(-1, 30) @ W1 + b1)\n","logits = h @ W2 + b2 # (32, 27)\n","loss = F.cross_entropy(logits, Ydev)\n","loss"]},{"cell_type":"markdown","metadata":{"id":"VflWeb6y3bBT"},"source":["Come possiamo vedere, già con questi cambiamenti, la perdita sul dataset di train inizia a separarsi da quella sul dataset di test, perciò probabilmente la nostra supposizione che il modello potesse eessere troppo piccolo potrebbe essere corretta.\n","\n","Per completezza, vediamo l'accuratezza anche sui dati di test."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1719612212994,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"DwOgCg1vdNhe","outputId":"9ba8fa4d-96bd-4699-e5dc-e4f5ec3e15df"},"outputs":[{"data":{"text/plain":["tensor(2.2003, grad_fn=<NllLossBackward0>)"]},"execution_count":186,"metadata":{},"output_type":"execute_result"}],"source":["# test loss\n","emb = C[Xte] # (32, 3, 2)\n","h = torch.tanh(emb.view(-1, 30) @ W1 + b1) # (32, 100)\n","logits = h @ W2 + b2 # (32, 27)\n","loss = F.cross_entropy(logits, Yte)\n","loss"]},{"cell_type":"markdown","metadata":{"id":"1JwXy9wE4IAz"},"source":["<a name=\"s7\"></a>\n","## **7 - Generazione di nomi**\n","\n","Infine, possiamo effettuare dei test di generazione di nomi per vedere come performa il modello:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":292,"status":"ok","timestamp":1719612267358,"user":{"displayName":"Josho Art","userId":"07468157323033204761"},"user_tz":-120},"id":"TQUMmgRrdRIA","outputId":"4242c3ee-9e06-4c4e-f115-a3dabf50009a"},"outputs":[{"name":"stdout","output_type":"stream","text":["mora.\n","mayah.\n","seel.\n","nihah.\n","lorethrucendraeg.\n","adeeydeeliigh.\n","poren.\n","eden.\n","sananaraelyn.\n","malaia.\n","noshibergihamies.\n","jenir.\n","jelionnteron.\n","ubukahdariyah.\n","fael.\n","yuma.\n","myston.\n","mahianna.\n","yansun.\n","zakelle.\n"]}],"source":["# sample from the model\n","g = torch.Generator().manual_seed(2147483647 + 10)\n","\n","for _ in range(20): # 20 generazioni\n","\n","    out = []\n","    context = [0] * block_size # inizializza con \"...\"\n","    while True: # il modello continua a generare finche non genera \"0\", ovvero \".\"\n","      emb = C[torch.tensor([context])] # (1,block_size,d)\n","      h = torch.tanh(emb.view(1, -1) @ W1 + b1)\n","      logits = h @ W2 + b2\n","      probs = F.softmax(logits, dim=1) # rende probabilità (con l'esponenziale) e fa in modo che sommano ad 1, e previene problemi allo stesso modo della funzione cross_entropy\n","      ix = torch.multinomial(probs, num_samples=1, generator=g).item() # seleziona dalle varie probabilità\n","      context = context[1:] + [ix] # viene tagliato il primo elemento del contesto , e si fa l'append con il numero del carattere attuale (quindi per il primo giro in questo esempio si passa da [0, 0, 0] a [0, 0, 5])\n","      out.append(ix)\n","      if ix == 0: # condizione per far fermare il ciclo while\n","        break\n","\n","    print(''.join(itos[i] for i in out)) # decoding degli indici"]},{"cell_type":"markdown","metadata":{"id":"yLpijtDz4Oyg"},"source":["Come possiamo constatare, le generazioni sono decisamente migliori rispetto al Bigram Language Model sviluppato nella lezione precedente."]},{"cell_type":"markdown","metadata":{},"source":["<a name=\"s8\"></a>\n","## **Conclusioni**\n","\n","Questa lezione ci ha permesso di esplorare concetti fondamentali per lo sviluppo di modelli di intelligenza artificiale generativa più sofisticati. In particolare, abbiamo costruito un modello linguistico N-Gram utilizzando una rete neurale di tipo Multi-Layer Perceptron (MLP) per la generazione di nomi, scegliendo arbitrariamente il contesto di caratteri per migliorare la qualità delle previsioni.\n","\n","Uno dei primi passi è stato l'utilizzo dell'embedding per rappresentare i caratteri. Questa tecnica ha permesso di trasformare i caratteri in vettori, permettendo al modello di catturare relazioni e caratteristiche particolari dei caratteri stessi.\n","\n","L'addestramento del modello ha mostrato una significativa riduzione della perdita rispetto al modello Bigram, indicando che il modello è riuscito ad apprendere efficacemente le relazioni tra i caratteri.\n","\n","Un aspetto interessante emerso è l'iniziale assenza di overfitting durante l'addestramento. Ciò ci ha suggerito che il modello non fosse ancora abbastanza potente da catturare completamente le relazioni complesse nei dati. Questa osservazione ci ha portato a considerare la possibilità di modificare gli iperparametri per aumentare la capacità del modello, permettendogli di apprendere più a fondo le strutture presenti nei dati senza causare overfitting. Per fare ciò, è stata essenziale la suddivisione del dataset in train, dev/validation e test. Questa pratica ha permesso di valutare meglio le performance del modello, garantendo inoltre che generalizzi bene su dati non visti.\n","\n","Abbiamo visto poi come rendere il processo di addestramento più efficiente, utilizzando funzioni di `PyTorch` come `view()` e `cross_entropy()`, e introducendo il concetto di batching, ovvero addestrare il modello solamente su piccole porzioni (random) del dataset iniziale, ad ogni iterazione, permettendo di gestire dataset più grandi e complessi in tempi ragionevoli.\n","\n","Infine, abbiamo visto come migliorare ancora di più le prestazioni del modello tramite l'introduzione del \"learning rate decay\", utile a migliorare ulteriormente la convergenza del modello.\n","\n","La lezione ha fornito una solida base per ulteriori esplorazioni e sviluppi nel campo dell'intelligenza artificiale generativa, aprendo la strada a studi più avanzati."]}],"metadata":{"colab":{"provenance":[{"file_id":"1YIfmkftLrz6MPTOO9Vwqrop2Q5llHIGK","timestamp":1719064424787}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}