第N11周：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
本周任务：
- 根据之前的内容，为解码器添加注意力机制
from __future__ import unicode_literals, print_function, division
from io import open
import unicodedata
import string
import re
import random
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
一、前期准备工作
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 = name
self.word2index = {}
self.word2count = {}
self.index2word = {0: "SOS", 1: "EOS"}
self.n_words = 2 # Count SOS and EOS
def 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_words
self.word2count[word] = 1
self.index2word[self.n_words] = word
self.n_words += 1
else:
self.word2count[word] += 1
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
3. 文件读取函数
def readLangs(lang1, lang2, reverse=False):
print("Reading lines...")
# 以行为单位读取文件
lines = open('%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, pairs
input_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_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):
return torch.zeros(1, 1, self.hidden_size, device=device)
2. 解码器（AttnDecoder）
class AttnDecoderRNN(nn.Module):
def __init__(self, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):
super(AttnDecoderRNN, 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):
return torch.zeros(1,1,self.hidden_size,device=device)
三、训练
1. 数据预处理
# 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)
2. 训练函数
teacher_forcing_ratio = 0.5
def 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_hidden
use_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 input
for di in range(target_length):
decoder_output, decoder_hidden, decoder_attentiton = decoder(decoder_input, decoder_hidden, encoder_outputs)
loss += criterion(decoder_output, target_tensor[di])
decoder_input = target_tensor[di] # Teacher forcing
else:
# Without teacher forcing: use its own predictions as the next input
for di in range(target_length):
decoder_output, decoder_hidden, decoder_attentiton = decoder(decoder_input, decoder_hidden, encoder_outputs)
topv, topi = decoder_output.topk(1)
decoder_input = topi.squeeze().detach() # detach from history as input
loss += criterion(decoder_output, target_tensor[di])
if decoder_input.item() == EOS_token:
break
loss.backward()
encoder_optimizer.step()
decoder_optimizer.step()
return loss.item() / target_length
import time
import math
def asMinutes(s):
m = math.floor(s / 60)
s -= m * 60
return '%dm %ds' % (m, s)
def timeSince(since, percent):
now = time.time()
s = now - since
es = s / (percent)
rs = es - s
return '%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_every
plot_loss_total = 0 # Reset every plot_every
encoder_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 += loss
plot_loss_total += loss
if iter % print_every == 0:
print_loss_avg = print_loss_total / print_every
print_loss_total = 0
print('%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_every
plot_losses.append(plot_loss_avg)
plot_loss_total = 0
return plot_losses
3、评估
def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):
with torch.no_grad():
input_tensor = tensorFromSentence(input_lang, sentence)
input_length = input_tensor.size()[0]
encoder_hidden = encoder.initHidden()
encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)
for ei in range(input_length):
encoder_output, encoder_hidden = encoder(input_tensor[ei],encoder_hidden)
encoder_outputs[ei] += encoder_output[0,0]
decoder_input = torch.tensor([[SOS_token]], device=device)
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)
if topi.item() == EOS_token:
decoded_words.append('<EOS>')
break
else:
decoded_words.append(output_lang.index2word[topi.item()])
decoder_input = topi.squeeze().detach()
return decoded_words, decoder_attentions[:di + 1]
def evaluateRandomly(encoder, decoder, n=5):
for i in range(n):
pair = random.choice(pairs)
print('>',pair[0])
print('=',pair[1])
output_words, attentions = evaluate(encoder, decoder, pair[0])
output_sentence = ' '.join(output_words)
print('<',output_sentence)
print('')
四、训练与评估
hidden_size = 256
encoder1 = EncoderRNN(input_lang.n_words, hidden_size).to(device)
attn_decoder1 = AttnDecoderRNN(hidden_size, output_lang.n_words,dropout_p=0.1).to(device)
plot_losses = trainIters(encoder1, attn_decoder1, 10000, print_every=5000)
1m 38s (- 1m 38s) (5000 50%) 3.0798
3m 15s (- 0m 0s) (10000 100%) 2.5810
evaluateRandomly(encoder1, attn_decoder1)
> je suis desolee si je vous ai effrayee.
= i m sorry if i frightened you.
< i m sorry if i disturbed you. <EOS>
> je me rejouis de voir que tu vas bien.
= i m glad to see you re ok.
< i m afraid to have you you are. <EOS>
> vous n etes pas le bienvenu ici.
= you re not welcome here.
< you re not very here. <EOS>
> nous achetons.
= we re buying.
< we re in of of <EOS>
> je ne suis pas occupe non plus.
= i m not busy either.
< i m not sure that <EOS>
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()
可视化注意力图
import matplotlib.pyplot as plt
output_words, attentions = evaluate(encoder1, attn_decoder1, 'je suis trop froid')
plt.matshow(attentions.numpy())
import matplotlib.ticker as ticker
import warnings
warnings.filterwarnings("ignore")
def showAttention(input_sentence, output_words, attentions):
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(attentions.numpy(), cmap='bone')
fig.colorbar(cax)
ax.set_xticklabels([''] + input_sentence.split(' ') + ['<EOS>'], rotation=90)
ax.set_yticklabels([''] + output_words)
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
plt.show()
def evaluateAndShowAttention(input_sentence):
output_words, attentions = evaluate(encoder1, attn_decoder1,input_sentence)
print('input =', input_sentence)
print('output =', ' '.join(output_words))
showAttention(input_sentence, output_words, attentions)
evaluateAndShowAttention('elle a cinq ans de moins que moi')
evaluateAndShowAttention('elle est trop petit')
evaluateAndShowAttention('je ne crains pas de mourir')
evaluateAndShowAttention('c est un jeune directeur plein de talent')
input = elle a cinq ans de moins que moi
output = she s looking years of a <EOS>
input = elle est trop petit
output = she s too <EOS>
input = je ne crains pas de mourir
output = i m not sure of <EOS>
input = c est un jeune directeur plein de talent
output = he is a of of <EOS>
总结
- 本周是在seq2seq的基础上添加了注意力机制
- 注意力机制就是将编码过程中的每一步与解码步骤结合之后计算出类似权重的东西，最后将解码步骤乘以权重
- 注意力机制能很好地处理解码过程中的累计误差，从而使输出更准确
原文地址：https://blog.csdn.net/a536723241/article/details/144261760
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