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08.language_model.py
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#!/usr/bin/env python
# coding: utf-8
# #语言模型
#
# - build language model
# - torchtext vocab
# - torch.nn
# - lstm
# - rnn
# - gru
# - linear
# - gradient clipping
# - save and read model
# In[2]:
import random
import numpy as np
import torch
import torchtext
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
random.seed(1)
np.random.seed(1)
torch.manual_seed(1)
MAX_VOCAB_SIZE = 10000
BATCH_SIZE = 32
EMBEDDING_SIZE = 20
# In[3]:
TEXT = torchtext.data.Field(lower=True)
train, val, test = torchtext.datasets.LanguageModelingDataset.splits(path='..',
train="./data/nietzsche.txt",
validation="./data/nietzsche.txt",
test="./data/nietzsche.txt",
text_field=TEXT)
# In[4]:
TEXT.build_vocab(train, max_size=MAX_VOCAB_SIZE)
print(f"vocab size {len(TEXT.vocab)}")
VOCAB_SIZE = len(TEXT.vocab)
# In[22]:
print(TEXT.vocab.itos[:10])
# In[23]:
print(TEXT.vocab.stoi["mother"])
train_iter, val_iter, test_iter = torchtext.data.BPTTIterator.splits((train, val, test),
batch_size=BATCH_SIZE,
device=device,
bptt_len=50,
repeat=False,
shuffle=True)
# In[28]:
batch = next(iter(train_iter))
print(" ".join(TEXT.vocab.itos[i] for i in batch.text[:, 0].data.cpu()))
print()
print(" ".join(TEXT.vocab.itos[i] for i in batch.target[:, 0].data.cpu()))
# ## 定义模型
import torch.nn as nn
class RNNModel(nn.Module):
def __init__(self, rnn_type, vocab_size, embedding_size, hidden_size, nlayers=1, dropout=0.5):
super(RNNModel, self).__init__()
self.encoder = nn.Embedding(vocab_size, embedding_size)
if rnn_type in ['LSTM', 'GRU']:
self.rnn = getattr(nn, rnn_type)(embedding_size, hidden_size, nlayers, dropout=dropout)
else:
raise ValueError("model type:['LSTM', 'GRU]")
self.decoder = nn.Linear(hidden_size, vocab_size)
self.drop = nn.Dropout(dropout)
self.init_weights()
self.rnn_type = rnn_type
self.hidden_size = hidden_size
self.nlayers = nlayers
def init_weights(self, init_range=0.1):
self.encoder.weight.data.uniform_(-init_range, init_range)
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-init_range, init_range)
def forward(self, x, hidden):
emb = self.drop(self.encoder(x))
output, hidden = self.rnn(emb, hidden)
output = self.drop(output)
decoded = self.decoder(output.view(output.size(0) * output.size(1), output.size(2)))
return decoded.view(output.size(0), output.size(1), decoded.size(1)), hidden
def init_hidden(self, batch_size, requires_grad=True):
weight = next(self.parameters())
if self.rnn_type == 'LSTM':
return (weight.new_zeros((self.nlayers, batch_size, self.hidden_size), requires_grad=requires_grad),
weight.new_zeros((self.nlayers, batch_size, self.hidden_size), requires_grad=requires_grad))
else:
return weight.new_zeros((self.nlayers, batch_size, self.hidden_size), requires_grad=requires_grad)
def repackage_hidden(h):
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)
loss_fn = nn.CrossEntropyLoss()
learning_rate = 0.001
def evaluate(model, data):
model.eval()
total_loss, total_count = 0., 0.
it = iter(data)
with torch.no_grad():
hidden = model.init_hidden(BATCH_SIZE, requires_grad=False)
for i, batch in enumerate(it):
data, target = batch.text.to(device), batch.target.to(device)
hidden = repackage_hidden(hidden)
with torch.no_grad():
output, hidden = model(data, hidden)
loss = loss_fn(output.view(-1, VOCAB_SIZE), target.view(-1))
total_count += np.multiply(*data.size())
total_loss += loss.item() * np.multiply(*data.size())
loss = total_loss / total_count
model.train()
return loss
GRAD_CLIP = 1.
NUM_EPOCHS = 2
MODEL_PATH = 'lm_best.pth'
model = RNNModel('LSTM', VOCAB_SIZE, EMBEDDING_SIZE, EMBEDDING_SIZE, 2, dropout=0.5).to(device)
print(model)
def train():
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, 0.5)
val_losses = []
for epoch in range(NUM_EPOCHS):
model.train()
it = iter(train_iter)
hidden = model.init_hidden(BATCH_SIZE)
for i, batch in enumerate(it):
data, target = batch.text.to(device), batch.target.to(device)
hidden = repackage_hidden(hidden)
model.zero_grad()
output, hidden = model(data, hidden)
loss = loss_fn(output.view(-1, VOCAB_SIZE), target.view(-1))
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
optimizer.step()
if i % 1000 == 0:
print('epoch', epoch, 'iter', i, 'loss', loss.item())
if i % 10000 == 0:
val_loss = evaluate(model, val_iter)
if len(val_losses) == 0 or val_loss < min(val_losses):
print('best model, val loss:', val_loss)
torch.save(model.state_dict(), MODEL_PATH)
else:
scheduler.step()
val_losses.append(val_loss)
# train()
model.load_state_dict(torch.load(MODEL_PATH))
val_loss = evaluate(model, val_iter)
print('val perplexity:', np.exp(val_loss))
test_loss = evaluate(model, test_iter)
print('test perplexity:', np.exp(val_loss))
def generate_text():
hidden = model.init_hidden(1)
x = torch.randint(VOCAB_SIZE, (1, 1), dtype=torch.long).to(device)
words = []
for i in range(100):
output, hidden = model(x, hidden)
word_weights = output.squeeze().exp().cpu()
word_idx = torch.multinomial(word_weights, 1)[0]
x.fill_(word_idx)
word = TEXT.vocab.itos[word_idx]
words.append(word)
result = ' '.join(words)
return result
text = generate_text()
print(text)