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