深度学习之 seq2seq 进行 英文到法文的翻译
深度学习之 seq2seq 进行 英文到法文的翻译
import os
import torch
import random
source_path = "data/small_vocab_en"
target_path = "data/small_vocab_fr"
MAX_LENGTH = 100
SOS_token = 0
EOS_token = 1
def load_data(path):
input_file = os.path.join(path)
with open(input_file, 'r', encoding='utf-8') as f:
data = f.read()
return data
source_text = load_data(source_path)
target_text = load_data(target_path)
class Dictionary(object):
def __init__(self):
self.word2idx = {'<SOS>': 0, '<EOS>': 1}
self.idx2word = {0: '<SOS>', 1: '<EOS>'}
self.count = 2
def add_word(self, word):
if word not in self.word2idx:
self.idx2word[self.count - 1] = word
self.word2idx[word] = len(self.idx2word) - 1
self.count += 1
return self.word2idx[word]
def __len__(self):
return len(self.idx2word)
class Lang(object):
def __init__(self, name):
self.name = name
self.dictionary = Dictionary()
def addSentence(self, sentence):
return [self.addWord(w) for w in sentence.split()]
def addWord(self, word):
return self.dictionary.add_word(word)
def __len__(self):
return len(self.dictionary)
def readLangs(source_name, source_lang_text, target_name, target_lang_text):
source_lang = Lang(source_name)
source_data = [source_lang.addSentence(s) for s in source_lang_text.lower().split('\n')]
target_lang = Lang(target_name)
target_sentences = [ s + ' <EOS>' for s in target_lang_text.lower().split('\n')]
target_data = [target_lang.addSentence(s) for s in target_sentences]
pairs = list(zip(source_data, target_data))
return source_lang, target_lang, pairs
source_lang, target_lang, pairs_data = readLangs('en', source_text, 'fe', target_text)
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
class EncoderRNN(nn.Module):
def __init__(self, input_size, hidden_size):
super(EncoderRNN, self).__init__()
self.hidden_size = hidden_size
self.embedding = nn.Embedding(input_size, hidden_size)
self.gru = nn.GRU(hidden_size, hidden_size)
def forward(self, input, hidden):
embedded = self.embedding(input).view(1, 1, -1)
output = embedded
output, hidden = self.gru(output, hidden)
return output, hidden
def initHidden(self):
result = Variable(torch.zeros(1, 1, self.hidden_size))
return result
class DecoderRNN(nn.Module):
def __init__(self, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):
super(DecoderRNN, self).__init__()
self.hidden_size = hidden_size
self.output_size = output_size
self.dropout_p = dropout_p
self.max_length = max_length
self.embedding = nn.Embedding(self.output_size, self.hidden_size)
self.attn = nn.Linear(self.hidden_size * 2, self.max_length)
self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size)
self.dropout = nn.Dropout(self.dropout_p)
self.gru = nn.GRU(self.hidden_size, self.hidden_size)
self.out = nn.Linear(self.hidden_size, self.output_size)
def forward(self, input, hidden, encoder_outputs):
embedded = self.embedding(input).view(1, 1, -1)
embedded = self.dropout(embedded)
attn_weights = F.softmax(self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1)
attn_applied = torch.bmm(attn_weights.unsqueeze(0), encoder_outputs.unsqueeze(0))
output = torch.cat((embedded[0], attn_applied[0]), 1)
output = self.attn_combine(output).unsqueeze(0)
output = F.relu(output)
output, hidden = self.gru(output, hidden)
output = F.log_softmax(self.out(output[0]), dim=1)
return output, hidden, attn_weights
def initHidden(self):
result = Variable(torch.zeros(1, 1, self.hidden_size))
return result
epochs = 10
print_every = 2
hidden_size = 256
teacher_forcing_ratio = 0.5
encoder_model = EncoderRNN(len(source_lang), hidden_size)
att_decoder_model = DecoderRNN(hidden_size, len(target_lang), dropout_p=0.1)
def variablesFromIds(ids):
return Variable(torch.LongTensor(ids).view(-1, 1))
def variablesFromPair(pair):
input_var = variablesFromIds(pair[0])
output_var = variablesFromIds(pair[1])
return (input_var, output_var)
def train(input, target, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length=MAX_LENGTH):
encoder_hidden = encoder.initHidden()
encoder_optimizer.zero_grad()
decoder_optimizer.zero_grad()
input_length = input.size()[0]
target_length = target.size()[0]
encoder_outputs = Variable(torch.zeros(max_length, encoder.hidden_size))
loss = 0
for i in range(input_length):
encoder_output, encoder_hidden = encoder(input[i], encoder_hidden)
encoder_outputs[i] = encoder_output[0][0]
decoder_input = Variable(torch.LongTensor([[SOS_token]]))
decoder_hidden = encoder_hidden
use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False
if use_teacher_forcing:
for di in range(target_length):
decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)
loss += criterion(decoder_output, target[di])
decoder_input = target[di]
else:
for di in range(target_length):
decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)
topv, topi = decoder_output.data.topk(1)
ni = topi[0][0]
decoder_input = Variable(torch.LongTensor([[ni]]))
loss += criterion(decoder_output, target[di])
if ni == EOS_token:
break;
loss.backward()
encoder_optimizer.step()
decoder_optimizer.step()
return loss.data[0] / target_length
def trainIters(encoder, decoder, n_iters, print_every=10, learning_rate=0.01):
encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)
training_pairs = [variablesFromPair(random.choice(pairs_data)) for i in range(n_iters)]
criterion = nn.NLLLoss()
total_loss = 0
for iter in range(1, n_iters + 1):
training_pair = training_pairs[iter - 1]
input_variable = training_pair[0]
target_variable = training_pair[1]
loss = train(input_variable, target_variable, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion)
total_loss += loss
if iter % print_every == 0:
print('(%d %d%%) loss %d total-loss %d percent %.4f' % (iter, iter / n_iters * 100, loss ,total_loss, total_loss / print_every))
trainIters(encoder_model, att_decoder_model, 5000)
def evaluate(encoder, decoder, sentence, max_length = MAX_LENGTH):
input_variable = variablesFromIds(sentence)
input_length = input_variable.size()[0]
encoder_hidden = encoder.initHidden()
encoder_outputs = Variable(torch.zeros(max_length, encoder.hidden_size))
for ei in range(input_length):
encoder_output, encoder_hidden = encoder(input_variable[ei], encoder_hidden)
encoder_outputs[ei] = encoder_outputs[ei] + encoder_output[0][0]
decoder_input = Variable(torch.LongTensor([[SOS_token]])) # SOS
decoder_hidden = encoder_hidden
decoded_words = []
decoder_attentions = torch.zeros(max_length, max_length)
for di in range(max_length):
decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)
decoder_attentions[di] = decoder_attention.data
topv, topi = decoder_output.data.topk(1)
ni = topi[0][0]
if ni == EOS_token:
decoded_words.append('<EOS>')
break
else:
decoded_words.append(target_lang.dictionary.idx2word[ni])
decoder_input = Variable(torch.LongTensor([[ni]]))
return decoded_words, decoder_attentions[:di + 1]
evaluateRandomly(encoder_model, att_decoder_model)
结论
训练少，正确率较低，后面再实现一个对话机器人
