- 🍨 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 | 接辅导、项目定制
一、前期准备
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)
1、搭建语言类
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
定义了一个名为Lang的类,用于处理语料库。Lang类包含了两个函数,addSentence和addWord,用于向语料库中添加句子和单词。类中包含了一些属性,word2index、word2count、index2word、n_words,分别用于存储单词到索引的映射、单词累计次数、索引到单词的映射以及单词总数。
2、文本处理函数
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
unicodeToAscii
函数的通过使用unicodedata.normalize('NFD', s)
将字符串进行规范化分解,然后通过列表推导式保留所有不属于 'Mn' 类别的字符,最后将这些字符拼接成一个新的字符串返回。
normalizeString
函数先调用unicodeToAscii
函数将输入的字符串转换为 ASCII 字符串,然后使用正则表达式替换掉所有的句号、感叹号和问号,以及所有非字母、非空格、非句号、非感叹号、非问号的字符,最后返回处理后的字符串。
3、文件读取函数
def readLangs(lang1,lang2,reverse=False):print("Reading lines...")lines = open('D:/%s-%s.txt'%(lang1,lang2),encoding='utf-8').\read().strip().split('\n')pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines]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
readLangs
接受三个参数:lang1
、lang2
和reverse
。函数的主要功能是从一个文本文件中读取语言对数据,并根据需要对数据进行预处理。
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):input_lang,output_lang,pairs = readLangs(lang1,lang2,reverse)print("Read %s sentence pairs" % len(pairs))pairs = filterPairs(pairs[:])print("Trimmed to %s sentence pairs" % len(pairs))print("Counting words...")for pair in pairs:input_lang.addSentence(pair[0])output_lang.addSentence(pair[1])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))
二、Seq2Seq模型
1.编码器(Encoder)
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)
2.解码器(Decoder)
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)
三、训练
1.数据预处理
#将句子中的每个单词转换为对应的索引值,并将这些索引值存储在一个列表中。
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)
2.训练函数
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
在序列生成的任务中,如机器翻译或文本生成,解码器(decoder)的输入通常是由解码器自己生成的预测结果,即前一个时间步的输出。然而,这种自回归方式可能存在一个问题,即在训练过程中,解码器可能会产生累积误差,并导致输出与目标序列逐渐偏离。
为了解决这个问题,引入了一种称为"Teacher Forcing"的技术。在训练过程中,Teacher Forcing将目标序列的真实值作为解码器的输入,而不是使用解码器自己的预测结果。这样可以提供更准确的指导信号,帮助解码器更快地学习到正确的输出。
在这段代码中,use_teacher_forcing变量用于确定解码器在训练阶段使用何种策略作为下一个输入。
当use_teacher_forcing为True时,采用"Teacher Forcing"的策略,即将目标序列中的真实标签作为解码器的下一个输入。而当use_teacher_forcing为False时,采用"Without Teacher Forcing"的策略,即将解码器自身的预测作为下一个输入。
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
四、训练与评估
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, 100000, print_every=5000)
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()