104 lines
2.7 KiB
Python
104 lines
2.7 KiB
Python
import pickle
|
|
|
|
from torch.utils.data import IterableDataset
|
|
import itertools
|
|
from torch import nn
|
|
import torch
|
|
import lzma
|
|
from torch.utils.data import DataLoader
|
|
|
|
import tqdm
|
|
|
|
vocabulary_size = 20000
|
|
|
|
vocab = None
|
|
with open('vocabulary.pickle', 'rb') as handle:
|
|
vocab = pickle.load(handle)
|
|
|
|
def look_ahead_iterator(gen):
|
|
prev = None
|
|
for item in gen:
|
|
if prev is not None:
|
|
yield (prev, item)
|
|
prev = item
|
|
|
|
def get_words_from_line(line):
|
|
line = line.rstrip()
|
|
yield '<s>'
|
|
for t in line.split(' '):
|
|
yield t
|
|
yield '</s>'
|
|
|
|
def get_word_lines_from_file(file_name):
|
|
with lzma.open(file_name, 'r') as fh:
|
|
for line in fh:
|
|
yield get_words_from_line(line.decode('utf-8'))
|
|
|
|
class Bigrams(IterableDataset):
|
|
def __init__(self, text_file, vocabulary_size):
|
|
self.vocab = vocab
|
|
self.vocab.set_default_index(self.vocab['<unk>'])
|
|
self.vocabulary_size = vocabulary_size
|
|
self.text_file = text_file
|
|
|
|
def __iter__(self):
|
|
return look_ahead_iterator(
|
|
(self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))
|
|
|
|
train_dataset = Bigrams('train/in.tsv.xz', vocabulary_size)
|
|
|
|
# print(next(iter(train_dataset)))
|
|
#
|
|
# print(vocab.lookup_tokens([23, 0]))
|
|
|
|
embed_size = 100
|
|
|
|
class SimpleBigramNeuralLanguageModel(nn.Module):
|
|
def __init__(self, vocabulary_size, embedding_size):
|
|
super(SimpleBigramNeuralLanguageModel, self).__init__()
|
|
self.model = nn.Sequential(
|
|
nn.Embedding(vocabulary_size, embedding_size),
|
|
nn.Linear(embedding_size, vocabulary_size),
|
|
nn.Softmax()
|
|
)
|
|
|
|
def forward(self, x):
|
|
return self.model(x)
|
|
|
|
device = 'cuda'
|
|
model = SimpleBigramNeuralLanguageModel(vocabulary_size, embed_size).to(device)
|
|
data = DataLoader(train_dataset, batch_size=500)
|
|
optimizer = torch.optim.Adam(model.parameters())
|
|
criterion = torch.nn.NLLLoss()
|
|
|
|
model.train()
|
|
step = 0
|
|
for x, y in tqdm.tqdm(data):
|
|
x = x.to(device)
|
|
y = y.to(device)
|
|
optimizer.zero_grad()
|
|
ypredicted = model(x)
|
|
loss = criterion(torch.log(ypredicted), y)
|
|
if step % 100 == 0:
|
|
print(step, loss)
|
|
if step > 5000:
|
|
break
|
|
step += 1
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
torch.save(model.state_dict(), 'model1.bin')
|
|
|
|
device = 'cuda'
|
|
model = SimpleBigramNeuralLanguageModel(vocabulary_size, embed_size).to(device)
|
|
model.load_state_dict(torch.load('model1.bin'))
|
|
model.eval()
|
|
|
|
ixs = torch.tensor(vocab.forward(['that'])).to(device)
|
|
|
|
out = model(ixs)
|
|
top = torch.topk(out[0], 10)
|
|
top_indices = top.indices.tolist()
|
|
top_probs = top.values.tolist()
|
|
top_words = vocab.lookup_tokens(top_indices)
|
|
print(list(zip(top_words, top_indices, top_probs))) |