mirror of
https://github.com/yunjey/pytorch-tutorial.git
synced 2025-07-07 18:14:17 +08:00
change folder names
This commit is contained in:
yunjey
42068
tutorials/08 - Language Model/data/train.txt
Normal file
42068
tutorials/08 - Language Model/data/train.txt
Normal file
File diff suppressed because it is too large
Load Diff
46
tutorials/08 - Language Model/data_utils.py
Normal file
46
tutorials/08 - Language Model/data_utils.py
Normal file
@ -0,0 +1,46 @@
|
||||
import torch
|
||||
import os
|
||||
|
||||
class Dictionary(object):
|
||||
def __init__(self):
|
||||
self.word2idx = {}
|
||||
self.idx2word = {}
|
||||
self.idx = 0
|
||||
|
||||
def add_word(self, word):
|
||||
if not word in self.word2idx:
|
||||
self.word2idx[word] = self.idx
|
||||
self.idx2word[self.idx] = word
|
||||
self.idx += 1
|
||||
|
||||
def __len__(self):
|
||||
return len(self.word2idx)
|
||||
|
||||
class Corpus(object):
|
||||
def __init__(self, path='./data'):
|
||||
self.dictionary = Dictionary()
|
||||
self.train = os.path.join(path, 'train.txt')
|
||||
self.test = os.path.join(path, 'test.txt')
|
||||
|
||||
def get_data(self, path, batch_size=20):
|
||||
# Add words to the dictionary
|
||||
with open(path, 'r') as f:
|
||||
tokens = 0
|
||||
for line in f:
|
||||
words = line.split() + ['<eos>']
|
||||
tokens += len(words)
|
||||
for word in words:
|
||||
self.dictionary.add_word(word)
|
||||
|
||||
# Tokenize the file content
|
||||
ids = torch.LongTensor(tokens)
|
||||
token = 0
|
||||
with open(path, 'r') as f:
|
||||
for line in f:
|
||||
words = line.split() + ['<eos>']
|
||||
for word in words:
|
||||
ids[token] = self.dictionary.word2idx[word]
|
||||
token += 1
|
||||
num_batches = ids.size(0) // batch_size
|
||||
ids = ids[:num_batches*batch_size]
|
||||
return ids.view(batch_size, -1)
|
||||
123
tutorials/08 - Language Model/main-gpu.py
Normal file
123
tutorials/08 - Language Model/main-gpu.py
Normal file
@ -0,0 +1,123 @@
|
||||
# Some part of the code was referenced from below.
|
||||
# https://github.com/pytorch/examples/tree/master/word_language_model
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from torch.autograd import Variable
|
||||
from data_utils import Dictionary, Corpus
|
||||
|
||||
# Hyper Parameters
|
||||
embed_size = 128
|
||||
hidden_size = 1024
|
||||
num_layers = 1
|
||||
num_epochs = 5
|
||||
num_samples = 1000 # number of words to be sampled
|
||||
batch_size = 20
|
||||
seq_length = 30
|
||||
learning_rate = 0.002
|
||||
|
||||
# Load Penn Treebank Dataset
|
||||
train_path = './data/train.txt'
|
||||
sample_path = './sample.txt'
|
||||
corpus = Corpus()
|
||||
ids = corpus.get_data(train_path, batch_size)
|
||||
vocab_size = len(corpus.dictionary)
|
||||
num_batches = ids.size(1) // seq_length
|
||||
|
||||
# RNN Based Language Model
|
||||
class RNNLM(nn.Module):
|
||||
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
|
||||
super(RNNLM, self).__init__()
|
||||
self.embed = nn.Embedding(vocab_size, embed_size)
|
||||
self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
|
||||
self.linear = nn.Linear(hidden_size, vocab_size)
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def init_weights(self):
|
||||
self.embed.weight.data.uniform_(-0.1, 0.1)
|
||||
self.linear.bias.data.fill_(0)
|
||||
self.linear.weight.data.uniform_(-0.1, 0.1)
|
||||
|
||||
def forward(self, x, h):
|
||||
# Embed word ids to vectors
|
||||
x = self.embed(x)
|
||||
|
||||
# Forward propagate RNN
|
||||
out, h = self.lstm(x, h)
|
||||
|
||||
# Reshape output to (batch_size*sequence_length, hidden_size)
|
||||
out = out.contiguous().view(out.size(0)*out.size(1), out.size(2))
|
||||
|
||||
# Decode hidden states of all time step
|
||||
out = self.linear(out)
|
||||
return out, h
|
||||
|
||||
model = RNNLM(vocab_size, embed_size, hidden_size, num_layers)
|
||||
model.cuda()
|
||||
|
||||
# Loss and Optimizer
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
|
||||
|
||||
# Truncated Backpropagation
|
||||
def detach(states):
|
||||
return [Variable(state.data) for state in states]
|
||||
|
||||
# Training
|
||||
for epoch in range(num_epochs):
|
||||
# Initial hidden and memory states
|
||||
states = (Variable(torch.zeros(num_layers, batch_size, hidden_size)).cuda(),
|
||||
Variable(torch.zeros(num_layers, batch_size, hidden_size)).cuda())
|
||||
|
||||
for i in range(0, ids.size(1) - seq_length, seq_length):
|
||||
# Get batch inputs and targets
|
||||
inputs = Variable(ids[:, i:i+seq_length]).cuda()
|
||||
targets = Variable(ids[:, (i+1):(i+1)+seq_length].contiguous()).cuda()
|
||||
|
||||
# Forward + Backward + Optimize
|
||||
model.zero_grad()
|
||||
states = detach(states)
|
||||
outputs, states = model(inputs, states)
|
||||
loss = criterion(outputs, targets.view(-1))
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm(model.parameters(), 0.5)
|
||||
optimizer.step()
|
||||
|
||||
step = (i+1) // seq_length
|
||||
if step % 100 == 0:
|
||||
print ('Epoch [%d/%d], Step[%d/%d], Loss: %.3f, Perplexity: %5.2f' %
|
||||
(epoch+1, num_epochs, step, num_batches, loss.data[0], np.exp(loss.data[0])))
|
||||
|
||||
# Sampling
|
||||
with open(sample_path, 'w') as f:
|
||||
# Set intial hidden ane memory states
|
||||
state = (Variable(torch.zeros(num_layers, 1, hidden_size)).cuda(),
|
||||
Variable(torch.zeros(num_layers, 1, hidden_size)).cuda())
|
||||
|
||||
# Select one word id randomly
|
||||
prob = torch.ones(vocab_size)
|
||||
input = Variable(torch.multinomial(prob, num_samples=1).unsqueeze(1),
|
||||
volatile=True).cuda()
|
||||
|
||||
for i in range(num_samples):
|
||||
# Forward propagate rnn
|
||||
output, state = model(input, state)
|
||||
|
||||
# Sample a word id
|
||||
prob = output.squeeze().data.exp().cpu()
|
||||
word_id = torch.multinomial(prob, 1)[0]
|
||||
|
||||
# Feed sampled word id to next time step
|
||||
input.data.fill_(word_id)
|
||||
|
||||
# File write
|
||||
word = corpus.dictionary.idx2word[word_id]
|
||||
word = '\n' if word == '<eos>' else word + ' '
|
||||
f.write(word)
|
||||
|
||||
if (i+1) % 100 == 0:
|
||||
print('Sampled [%d/%d] words and save to %s'%(i+1, num_samples, sample_path))
|
||||
|
||||
# Save the Trained Model
|
||||
torch.save(model, 'model.pkl')
|
||||
123
tutorials/08 - Language Model/main.py
Normal file
123
tutorials/08 - Language Model/main.py
Normal file
@ -0,0 +1,123 @@
|
||||
# Some part of the code was referenced from below.
|
||||
# https://github.com/pytorch/examples/tree/master/word_language_model
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from torch.autograd import Variable
|
||||
from data_utils import Dictionary, Corpus
|
||||
|
||||
# Hyper Parameters
|
||||
embed_size = 128
|
||||
hidden_size = 1024
|
||||
num_layers = 1
|
||||
num_epochs = 5
|
||||
num_samples = 1000 # number of words to be sampled
|
||||
batch_size = 20
|
||||
seq_length = 30
|
||||
learning_rate = 0.002
|
||||
|
||||
# Load Penn Treebank Dataset
|
||||
train_path = './data/train.txt'
|
||||
sample_path = './sample.txt'
|
||||
corpus = Corpus()
|
||||
ids = corpus.get_data(train_path, batch_size)
|
||||
vocab_size = len(corpus.dictionary)
|
||||
num_batches = ids.size(1) // seq_length
|
||||
|
||||
# RNN Based Language Model
|
||||
class RNNLM(nn.Module):
|
||||
def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
|
||||
super(RNNLM, self).__init__()
|
||||
self.embed = nn.Embedding(vocab_size, embed_size)
|
||||
self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
|
||||
self.linear = nn.Linear(hidden_size, vocab_size)
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def init_weights(self):
|
||||
self.embed.weight.data.uniform_(-0.1, 0.1)
|
||||
self.linear.bias.data.fill_(0)
|
||||
self.linear.weight.data.uniform_(-0.1, 0.1)
|
||||
|
||||
def forward(self, x, h):
|
||||
# Embed word ids to vectors
|
||||
x = self.embed(x)
|
||||
|
||||
# Forward propagate RNN
|
||||
out, h = self.lstm(x, h)
|
||||
|
||||
# Reshape output to (batch_size*sequence_length, hidden_size)
|
||||
out = out.contiguous().view(out.size(0)*out.size(1), out.size(2))
|
||||
|
||||
# Decode hidden states of all time step
|
||||
out = self.linear(out)
|
||||
return out, h
|
||||
|
||||
model = RNNLM(vocab_size, embed_size, hidden_size, num_layers)
|
||||
|
||||
|
||||
# Loss and Optimizer
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
|
||||
|
||||
# Truncated Backpropagation
|
||||
def detach(states):
|
||||
return [Variable(state.data) for state in states]
|
||||
|
||||
# Training
|
||||
for epoch in range(num_epochs):
|
||||
# Initial hidden and memory states
|
||||
states = (Variable(torch.zeros(num_layers, batch_size, hidden_size)),
|
||||
Variable(torch.zeros(num_layers, batch_size, hidden_size)))
|
||||
|
||||
for i in range(0, ids.size(1) - seq_length, seq_length):
|
||||
# Get batch inputs and targets
|
||||
inputs = Variable(ids[:, i:i+seq_length])
|
||||
targets = Variable(ids[:, (i+1):(i+1)+seq_length].contiguous())
|
||||
|
||||
# Forward + Backward + Optimize
|
||||
model.zero_grad()
|
||||
states = detach(states)
|
||||
outputs, states = model(inputs, states)
|
||||
loss = criterion(outputs, targets.view(-1))
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm(model.parameters(), 0.5)
|
||||
optimizer.step()
|
||||
|
||||
step = (i+1) // seq_length
|
||||
if step % 100 == 0:
|
||||
print ('Epoch [%d/%d], Step[%d/%d], Loss: %.3f, Perplexity: %5.2f' %
|
||||
(epoch+1, num_epochs, step, num_batches, loss.data[0], np.exp(loss.data[0])))
|
||||
|
||||
# Sampling
|
||||
with open(sample_path, 'w') as f:
|
||||
# Set intial hidden ane memory states
|
||||
state = (Variable(torch.zeros(num_layers, 1, hidden_size)),
|
||||
Variable(torch.zeros(num_layers, 1, hidden_size)))
|
||||
|
||||
# Select one word id randomly
|
||||
prob = torch.ones(vocab_size)
|
||||
input = Variable(torch.multinomial(prob, num_samples=1).unsqueeze(1),
|
||||
volatile=True)
|
||||
|
||||
for i in range(num_samples):
|
||||
# Forward propagate rnn
|
||||
output, state = model(input, state)
|
||||
|
||||
# Sample a word id
|
||||
prob = output.squeeze().data.exp()
|
||||
word_id = torch.multinomial(prob, 1)[0]
|
||||
|
||||
# Feed sampled word id to next time step
|
||||
input.data.fill_(word_id)
|
||||
|
||||
# File write
|
||||
word = corpus.dictionary.idx2word[word_id]
|
||||
word = '\n' if word == '<eos>' else word + ' '
|
||||
f.write(word)
|
||||
|
||||
if (i+1) % 100 == 0:
|
||||
print('Sampled [%d/%d] words and save to %s'%(i+1, num_samples, sample_path))
|
||||
|
||||
# Save the Trained Model
|
||||
torch.save(model, 'model.pkl')
|
||||
Reference in New Issue
Block a user