Modelowanie Języka

10. Model neuronowy rekurencyjny [ćwiczenia]

import torch
from torch import nn, optim
from torch.utils.data import DataLoader
import numpy as np
from collections import Counter
import re

device = 'cpu'

! wget https://wolnelektury.pl/media/book/txt/potop-tom-pierwszy.txt
! wget https://wolnelektury.pl/media/book/txt/potop-tom-drugi.txt
! wget https://wolnelektury.pl/media/book/txt/potop-tom-trzeci.txt

--2022-05-08 19:27:04--  https://wolnelektury.pl/media/book/txt/potop-tom-pierwszy.txt
Resolving wolnelektury.pl (wolnelektury.pl)... 51.83.143.148, 2001:41d0:602:3294::
Connecting to wolnelektury.pl (wolnelektury.pl)|51.83.143.148|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 877893 (857K) [text/plain]
Saving to: ‘potop-tom-pierwszy.txt.2’

potop-tom-pierwszy. 100%[===================>] 857,32K  --.-KB/s    in 0,07s   

2022-05-08 19:27:04 (12,0 MB/s) - ‘potop-tom-pierwszy.txt.2’ saved [877893/877893]

--2022-05-08 19:27:04--  https://wolnelektury.pl/media/book/txt/potop-tom-drugi.txt
Resolving wolnelektury.pl (wolnelektury.pl)... 51.83.143.148, 2001:41d0:602:3294::
Connecting to wolnelektury.pl (wolnelektury.pl)|51.83.143.148|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1087797 (1,0M) [text/plain]
Saving to: ‘potop-tom-drugi.txt.2’

potop-tom-drugi.txt 100%[===================>]   1,04M  --.-KB/s    in 0,08s   

2022-05-08 19:27:04 (12,9 MB/s) - ‘potop-tom-drugi.txt.2’ saved [1087797/1087797]

--2022-05-08 19:27:05--  https://wolnelektury.pl/media/book/txt/potop-tom-trzeci.txt
Resolving wolnelektury.pl (wolnelektury.pl)... 51.83.143.148, 2001:41d0:602:3294::
Connecting to wolnelektury.pl (wolnelektury.pl)|51.83.143.148|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 788219 (770K) [text/plain]
Saving to: ‘potop-tom-trzeci.txt.2’

potop-tom-trzeci.tx 100%[===================>] 769,75K  --.-KB/s    in 0,06s   

2022-05-08 19:27:05 (12,0 MB/s) - ‘potop-tom-trzeci.txt.2’ saved [788219/788219]

!cat potop-* > potop.txt

class Dataset(torch.utils.data.Dataset):
    def __init__(
            self,
            sequence_length,
    ):
        self.sequence_length = sequence_length
        self.words = self.load()
        self.uniq_words = self.get_uniq_words()

        self.index_to_word = {index: word for index, word in enumerate(self.uniq_words)}
        self.word_to_index = {word: index for index, word in enumerate(self.uniq_words)}

        self.words_indexes = [self.word_to_index[w] for w in self.words]

    def load(self):
        with open('potop.txt', 'r') as f_in:
            text = [x.rstrip() for x in f_in.readlines() if x.strip()]
            text = ' '.join(text).lower()
            text = re.sub('[^a-ząćęłńóśźż ]', '', text) 
            text = text.split(' ')
        return text
    
    
    def get_uniq_words(self):
        word_counts = Counter(self.words)
        return sorted(word_counts, key=word_counts.get, reverse=True)

    def __len__(self):
        return len(self.words_indexes) - self.sequence_length

    def __getitem__(self, index):
        return (
            torch.tensor(self.words_indexes[index:index+self.sequence_length]),
            torch.tensor(self.words_indexes[index+1:index+self.sequence_length+1]),
        )

dataset = Dataset(5)

dataset[200]

(tensor([  551,    18,    17,   255, 10748]),
 tensor([   18,    17,   255, 10748,    34]))

[dataset.index_to_word[x] for x in [   551,    18,    17,   255, 10748]]

['patrzył', 'tak', 'jak', 'człowiek', 'zbudzony']

[dataset.index_to_word[x] for x in [   18,    17,   255, 10748,    34]]

['tak', 'jak', 'człowiek', 'zbudzony', 'ze']

input_tensor = torch.tensor([[ 551,    18,    17,   255, 10748]], dtype=torch.int32).to(device)

#input_tensor = torch.tensor([[ 551,    18]], dtype=torch.int32).to(device)

class Model(nn.Module):
    def __init__(self, vocab_size):
        super(Model, self).__init__()
        self.lstm_size = 128
        self.embedding_dim = 128
        self.num_layers = 3

        self.embedding = nn.Embedding(
            num_embeddings=vocab_size,
            embedding_dim=self.embedding_dim,
        )
        self.lstm = nn.LSTM(
            input_size=self.lstm_size,
            hidden_size=self.lstm_size,
            num_layers=self.num_layers,
            dropout=0.2,
        )
        self.fc = nn.Linear(self.lstm_size, vocab_size)

    def forward(self, x, prev_state = None):
        embed = self.embedding(x)
        output, state = self.lstm(embed, prev_state)
        logits = self.fc(output)
        return logits, state

    def init_state(self, sequence_length):
        return (torch.zeros(self.num_layers, sequence_length, self.lstm_size).to(device),
                torch.zeros(self.num_layers, sequence_length, self.lstm_size).to(device))

model = Model(len(dataset)).to(device)

y_pred, (state_h, state_c) = model(input_tensor)

y_pred

tensor([[[ 0.0046, -0.0113,  0.0313,  ...,  0.0198, -0.0312,  0.0223],
         [ 0.0039, -0.0110,  0.0303,  ...,  0.0213, -0.0302,  0.0230],
         [ 0.0029, -0.0133,  0.0265,  ...,  0.0204, -0.0297,  0.0219],
         [ 0.0010, -0.0120,  0.0282,  ...,  0.0241, -0.0314,  0.0241],
         [ 0.0038, -0.0106,  0.0346,  ...,  0.0230, -0.0333,  0.0232]]],
       grad_fn=<AddBackward0>)

y_pred.shape

torch.Size([1, 5, 1187998])

def train(dataset, model, max_epochs, batch_size):
    model.train()

    dataloader = DataLoader(dataset, batch_size=batch_size)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    for epoch in range(max_epochs):
        for batch, (x, y) in enumerate(dataloader):
            optimizer.zero_grad()
            x = x.to(device)
            y = y.to(device)

            y_pred, (state_h, state_c) = model(x)
            loss = criterion(y_pred.transpose(1, 2), y)

            loss.backward()
            optimizer.step()

            print({ 'epoch': epoch, 'update in batch': batch, '/' : len(dataloader), 'loss': loss.item() })

model = Model(vocab_size = len(dataset.uniq_words)).to(device)
train(dataset, model, 1, 64)

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[0;31m---------------------------------------------------------------------------[0m
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[0;32m~/anaconda3/lib/python3.8/site-packages/torch/autograd/__init__.py[0m in [0;36mbackward[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)[0m
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def predict(dataset, model, text, next_words=5):
    model.eval()
    words = text.split(' ')
    state_h, state_c = model.init_state(len(words))

    for i in range(0, next_words):
        x = torch.tensor([[dataset.word_to_index[w] for w in words[i:]]]).to(device)
        y_pred, (state_h, state_c) = model(x, (state_h, state_c))

        last_word_logits = y_pred[0][-1]
        p = torch.nn.functional.softmax(last_word_logits, dim=0).detach().cpu().numpy()
        word_index = np.random.choice(len(last_word_logits), p=p)
        words.append(dataset.index_to_word[word_index])

    return words

predict(dataset, model, 'kmicic szedł')

['kmicic', 'szedł', 'zwycięzco', 'po', 'do', 'zlituj', 'i']

ZADANIE

Stworzyć sieć rekurencyjną GRU dla Challenging America word-gap prediction. Wymogi takie jak zawsze, zadanie widoczne na gonito

60 KiB Raw Blame History Unescape Escape

Modelowanie Języka

10. Model neuronowy rekurencyjny [ćwiczenia]

ZADANIE

60 KiB

Raw Blame History