seq2seq翻译实战-Pytorch复现

🍨 本文为🔗365天深度学习训练营中的学习记录博客
🍖 原作者：K同学啊

code

from __future__ import unicode_literals, print_function, division
from io import open
import unicodedata
import string
import re
import randomimport torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as Fdevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)

cpu

SOS_token = 0
EOS_token = 1# 语言类，方便对语料库进行操作
class Lang:def __init__(self, name):self.name = nameself.word2index = {}self.word2count = {}self.index2word = {0: "SOS", 1: "EOS"}self.n_words    = 2  # Count SOS and EOSdef addSentence(self, sentence):for word in sentence.split(' '):self.addWord(word)def addWord(self, word):if word not in self.word2index:self.word2index[word] = self.n_wordsself.word2count[word] = 1self.index2word[self.n_words] = wordself.n_words += 1else:self.word2count[word] += 1

def unicodeToAscii(s):return ''.join(c for c in unicodedata.normalize('NFD', s)if unicodedata.category(c) != 'Mn')# 小写化，剔除标点与非字母符号
def normalizeString(s):s = unicodeToAscii(s.lower().strip())s = re.sub(r"([.!?])", r" \1", s)s = re.sub(r"[^a-zA-Z.!?]+", r" ", s)return s

def readLangs(lang1, lang2, reverse=False):print("Reading lines...")# 以行为单位读取文件lines = open('./data/%s-%s.txt'%(lang1,lang2), encoding='utf-8').\read().strip().split('\n')# 将每一行放入一个列表中# 一个列表中有两个元素，A语言文本与B语言文本pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines]# 创建Lang实例，并确认是否反转语言顺序if reverse:pairs       = [list(reversed(p)) for p in pairs]input_lang  = Lang(lang2)output_lang = Lang(lang1)else:input_lang  = Lang(lang1)output_lang = Lang(lang2)return input_lang, output_lang, pairs

MAX_LENGTH = 10      # 定义语料最长长度eng_prefixes = ("i am ", "i m ","he is", "he s ","she is", "she s ","you are", "you re ","we are", "we re ","they are", "they re "
)def filterPair(p):return len(p[0].split(' ')) < MAX_LENGTH and \len(p[1].split(' ')) < MAX_LENGTH and p[1].startswith(eng_prefixes)def filterPairs(pairs):# 选取仅仅包含 eng_prefixes 开头的语料return [pair for pair in pairs if filterPair(pair)]

def prepareData(lang1, lang2, reverse=False):# 读取文件中的数据input_lang, output_lang, pairs = readLangs(lang1, lang2, reverse)print("Read %s sentence pairs" % len(pairs))# 按条件选取语料pairs = filterPairs(pairs[:])print("Trimmed to %s sentence pairs" % len(pairs))print("Counting words...")# 将语料保存至相应的语言类for pair in pairs:input_lang.addSentence(pair[0])output_lang.addSentence(pair[1])# 打印语言类的信息    print("Counted words:")print(input_lang.name, input_lang.n_words)print(output_lang.name, output_lang.n_words)return input_lang, output_lang, pairsinput_lang, output_lang, pairs = prepareData('eng', 'fra', True)
print(random.choice(pairs))

Reading lines…
Read 135842 sentence pairs
Trimmed to 10599 sentence pairs
Counting words…
Counted words:
fra 4345
eng 2803
[‘elles le font correctement .’, ‘they re doing it right .’]

class EncoderRNN(nn.Module):def __init__(self, input_size, hidden_size):super(EncoderRNN, self).__init__()self.hidden_size = hidden_sizeself.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         = embeddedoutput, hidden = self.gru(output, hidden)return output, hiddendef initHidden(self):return torch.zeros(1, 1, self.hidden_size, device=device)

class DecoderRNN(nn.Module):def __init__(self, hidden_size, output_size):super(DecoderRNN, self).__init__()self.hidden_size = hidden_sizeself.embedding   = nn.Embedding(output_size, hidden_size)self.gru         = nn.GRU(hidden_size, hidden_size)self.out         = nn.Linear(hidden_size, output_size)self.softmax     = nn.LogSoftmax(dim=1)def forward(self, input, hidden):output         = self.embedding(input).view(1, 1, -1)output         = F.relu(output)output, hidden = self.gru(output, hidden)output         = self.softmax(self.out(output[0]))return output, hiddendef initHidden(self):return torch.zeros(1, 1, self.hidden_size, device=device)

# 将文本数字化，获取词汇index
def indexesFromSentence(lang, sentence):return [lang.word2index[word] for word in sentence.split(' ')]# 将数字化的文本，转化为tensor数据
def tensorFromSentence(lang, sentence):indexes = indexesFromSentence(lang, sentence)indexes.append(EOS_token)return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)# 输入pair文本，输出预处理好的数据
def tensorsFromPair(pair):input_tensor  = tensorFromSentence(input_lang, pair[0])target_tensor = tensorFromSentence(output_lang, pair[1])return (input_tensor, target_tensor)

teacher_forcing_ratio = 0.5def train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length=MAX_LENGTH):# 编码器初始化encoder_hidden = encoder.initHidden()# grad属性归零encoder_optimizer.zero_grad()decoder_optimizer.zero_grad()input_length  = input_tensor.size(0)target_length = target_tensor.size(0)# 用于创建一个指定大小的全零张量（tensor），用作默认编码器输出encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)loss = 0# 将处理好的语料送入编码器for ei in range(input_length):encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)encoder_outputs[ei]            = encoder_output[0, 0]# 解码器默认输出decoder_input  = torch.tensor([[SOS_token]], device=device)decoder_hidden = encoder_hiddenuse_teacher_forcing = True if random.random() < teacher_forcing_ratio else False# 将编码器处理好的输出送入解码器if use_teacher_forcing:# Teacher forcing: Feed the target as the next inputfor di in range(target_length):decoder_output, decoder_hidden = decoder(decoder_input, decoder_hidden)loss         += criterion(decoder_output, target_tensor[di])decoder_input = target_tensor[di]  # Teacher forcingelse:# Without teacher forcing: use its own predictions as the next inputfor di in range(target_length):decoder_output, decoder_hidden = decoder(decoder_input, decoder_hidden)topv, topi    = decoder_output.topk(1)decoder_input = topi.squeeze().detach()  # detach from history as inputloss         += criterion(decoder_output, target_tensor[di])if decoder_input.item() == EOS_token:breakloss.backward()encoder_optimizer.step()decoder_optimizer.step()return loss.item() / target_length

import time
import mathdef asMinutes(s):m = math.floor(s / 60)s -= m * 60return '%dm %ds' % (m, s)def timeSince(since, percent):now = time.time()s = now - sincees = s / (percent)rs = es - sreturn '%s (- %s)' % (asMinutes(s), asMinutes(rs))

def trainIters(encoder,decoder,n_iters,print_every=1000,plot_every=100,learning_rate=0.01):start = time.time()plot_losses      = []print_loss_total = 0  # Reset every print_everyplot_loss_total  = 0  # Reset every plot_everyencoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)# 在 pairs 中随机选取 n_iters 条数据用作训练集training_pairs    = [tensorsFromPair(random.choice(pairs)) for i in range(n_iters)]criterion         = nn.NLLLoss()for iter in range(1, n_iters + 1):training_pair = training_pairs[iter - 1]input_tensor  = training_pair[0]target_tensor = training_pair[1]loss = train(input_tensor, target_tensor, encoder,decoder, encoder_optimizer, decoder_optimizer, criterion)print_loss_total += lossplot_loss_total  += lossif iter % print_every == 0:print_loss_avg   = print_loss_total / print_everyprint_loss_total = 0print('%s (%d %d%%) %.4f' % (timeSince(start, iter / n_iters),iter, iter / n_iters * 100, print_loss_avg))if iter % plot_every == 0:plot_loss_avg = plot_loss_total / plot_everyplot_losses.append(plot_loss_avg)plot_loss_total = 0return plot_losses

hidden_size   = 256
encoder1      = EncoderRNN(input_lang.n_words, hidden_size).to(device)
attn_decoder1 = DecoderRNN(hidden_size, output_lang.n_words).to(device)plot_losses = trainIters(encoder1, attn_decoder1, 20000, print_every=5000)

7m 2s (- 21m 6s) (5000 25%) 2.8981
13m 59s (- 13m 59s) (10000 50%) 2.3636
21m 3s (- 7m 1s) (15000 75%) 2.0134
28m 10s (- 0m 0s) (20000 100%) 1.7973

import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.filterwarnings("ignore")               # 忽略警告信息
# plt.rcParams['font.sans-serif']    = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False      # 用来正常显示负号
plt.rcParams['figure.dpi']         = 100        # 分辨率epochs_range = range(len(plot_losses))plt.figure(figsize=(8, 3))plt.subplot(1, 1, 1)
plt.plot(epochs_range, plot_losses, label='Training Loss')
plt.legend(loc='upper right')
plt.title('Training Loss')
plt.show()

在这里插入图片描述

总结

构建基于PyTorch的seq2seq翻译系统是一个综合性的过程，它首先涉及数据的收集与预处理，包括将源语言和目标语言的文本对转换为适合模型训练的格式，并构建相应的词汇表。随后，定义包含编码器（负责将源语言序列编码为上下文信息）和解码器（利用编码信息生成目标语言序列）的seq2seq模型架构。在训练阶段，通过迭代训练数据，优化模型参数以最小化翻译损失，如交叉熵损失，同时采用正则化技术和梯度裁剪来防止过拟合和梯度爆炸。最后，在独立的测试集上评估模型的翻译性能，通过计算如BLEU分数等指标来衡量其准确性和流畅度。这一过程不仅要求深入理解seq2seq模型和PyTorch框架，还需要细致的数据处理和模型调优策略，以构建出高效且准确的翻译系统。