Data set download address:
https://download.pytorch.org/tutorial/data.zip
download.pytorch.org
The data set is in the eng-fra.txt file, and each line is a pair of translations between English and French.
Run the following code and make sure
PyTorch=1.9.0
torchtext=0.10.0
Data flow in Encoder:
Data flow in Decoder:
Data flow with attention mechanism Decoder:
# Encoder-Decoder实现英法互译
from __future__ import unicode_literals, print_function, division
import random
import re
from io import open
import torch
import torch.nn as nn
import torch.nn.functional as F
import unicodedata
from torch import optim
# 获取可用设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 一句话的开始标志 start of string
SOS_token = 0
# 一句话的结尾标志 end of string
EOS_token = 1
# 要翻译的语言的包装类，包含了常用工具
class Lang:
def __init__(self, name):
# 名称
self.name = name
# 词语->索引
self.word2index = {}
# 词语->计数
self.word2count = {}
# 索引->词语
# 默认添加SOS,EOS
self.index2word = {0: "SOS", 1: "EOS"}
# 词语数
# 因为现在已经有 SOS,EOS 所以=2
self.n_words = 2 # Count SOS and EOS
# 添加一句话
def addSentence(self, sentence):
# 以空格分割这句话
# 然后取出每一个词语
for word in sentence.split(' '):
# 添加词语
self.addWord(word)
def addWord(self, word):
# 如果以前没有添加过这个词语
if word not in self.word2index:
# 索引从0开始
# 所以先赋值
# 最后self.n_words+=1
self.word2index[word] = self.n_words
self.word2count[word] = 1
self.index2word[self.n_words] = word
self.n_words += 1
else:
# 已经存在则计数+=1
self.word2count[word] += 1
# 将一个Unicoide编码的字符
# 转换为ASCII编码的字符
# 统一字符编码方便处理
# 将一个Unicode字符串（数据集中的）转换为一个ASCII字符串（输入模型中的）
# 数据标准化
# 一个Unicode字符可以用多种不同的ASCII字符表示
# 转换为统一的形式方便模型处理
def unicodeToAscii(s):
return ''.join(
# normalize() 第一个参数指定字符串标准化的方式。
# NFC表示字符使用单一编码优先，
# 而NFD表示字符应该分解为多个组合字符表示
# 先将输入的字符转换
# 然后再过滤
# Mn表示Mark
# 如果不是特殊标记
c for c in unicodedata.normalize('NFD', s)
if unicodedata.category(c) != 'Mn'
)
# 将字符串规范化
def normalizeString(s):
# s.lower()先转换为小写
# .strip()去除首尾的空格
# 转换为ASCII编码的形式
s = unicodeToAscii(s.lower().strip())
# 去除标点符号
s = re.sub(r"([.!?])", r" \1", s)
# 去除非字母
s = re.sub(r"[^a-zA-Z.!?]+", r" ", s)
return s
# 从数据集中读取一行数据
def readLangs(lang1, lang2, reverse=False):
print("Reading lines...")
# Read the file and split into lines
# 首先以utf-8的方式打开数据集文件
# read()读取
# strip()去除多余的空格
# 以\n分割读取到的内容，也就是分割出每一行
lines = open('data/%s-%s.txt' % (lang1, lang2), encoding='utf-8'). \
read().strip().split('\n')
# Split every line into pairs and normalize
# 对于数据集中的每一行for l in lines
# 将每一行以\t分割for s in l.split('\t')
# 对于分割出来的每一句话s,进行规范化
pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines]
# Reverse pairs, make Lang instances
# 如果要翻转数据集
# 什么意思呢，就是如果原数据集存放的是英语->法语
# 如果指定reverse
# 那么将它进行翻转,变成法语->英语
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
# 一句话的最大长度
MAX_LENGTH = 10
# 选取数据集中的一部分进行训练
# 选取带有一以下前缀的句子
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 "
)
# 按照最大长度
# 指定前缀
# 筛选数据集
def filterPair(p):
return len(p[0].split(' ')) < MAX_LENGTH and len(p[1].split(' ')) < MAX_LENGTH and (
p[0].startswith(eng_prefixes) or p[1].startswith(eng_prefixes))
def filterPairs(pairs):
return [pair for pair in pairs if filterPair(pair)]
# 读取数据集
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, pairs
input_lang, output_lang, pairs = prepareData('eng', 'fra', True)
print(random.choice(pairs))
# 定义Encoder
class EncoderRNN(nn.Module):
def __init__(self, input_size, hidden_size):
super(EncoderRNN, self).__init__()
self.hidden_size = hidden_size
# 词嵌入
self.embedding = nn.Embedding(input_size, hidden_size)
# GRU
# 因为前面将输入进行了词嵌入，所以输入维度是hidden_size
self.gru = nn.GRU(hidden_size, hidden_size)
# 前向传递，建立计算图
def forward(self, input, hidden):
# 改成[长度，批大小，嵌入维度]的格式
# 为什么这里长度，批大小都是1呢
# 因为后面我们是将一句话中的每一个词逐一输入到Encoder中的
# Decoder同理
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)
# 定义Decoder
class DecoderRNN(nn.Module):
def __init__(self, hidden_size, output_size):
super(DecoderRNN, self).__init__()
self.hidden_size = hidden_size
self.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, hidden
def initHidden(self):
return torch.zeros(1, 1, self.hidden_size, device=device)
# 定义带有注意力机制的Decoder
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)
# attention的输入是词嵌入向量和隐状态
# 所以输入维度是self.hidden_size*2
# 因为Decoder输出的句子长度不确定
# 所以这里输出维度直接取最大了
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)
# 将encoder的输出乘以注意力权重
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)
# 句子->索引
def indexesFromSentence(lang, sentence):
return [lang.word2index[word] for word in sentence.split(' ')]
# 将句子转换为张量
def tensorFromSentence(lang, sentence):
indexes = indexesFromSentence(lang, sentence)
indexes.append(EOS_token)
return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)
# 将数据集中的一个样本转换为张量
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 = 0.5
def train(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion,
max_length=MAX_LENGTH):
# 初始化Encoder的隐藏层
encoder_hidden = encoder.initHidden()
# 梯度清零
encoder_optimizer.zero_grad()
decoder_optimizer.zero_grad()
# 输入输出的长度
input_length = input_tensor.size(0)
target_length = target_tensor.size(0)
encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)
loss = 0
# 将一句话中的每个词语输入到Encoder中
for ei in range(input_length):
encoder_output, encoder_hidden = encoder(
input_tensor[ei], encoder_hidden)
# 获取每一步的输出
encoder_outputs[ei] = encoder_output[0, 0]
# decoder的输入是一个SOS标记
decoder_input = torch.tensor([[SOS_token]], device=device)
# 隐状态是Encoder的最后的隐状态输出
decoder_hidden = encoder_hidden
# 是否使用teacher_force的训练模式
use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False
if use_teacher_forcing:
# 如果指定了teacher_force训练模式
# decoder每一步的输入是真实target中的词语
for di in range(target_length):
decoder_output, decoder_hidden, decoder_attention = decoder(
decoder_input, decoder_hidden, encoder_outputs)
loss += criterion(decoder_output, target_tensor[di])
decoder_input = target_tensor[di]
else:
# 指没有指定teacher_force训练模式
# decoder的下一步的输入是decoder上一步的输出
for di in range(target_length):
decoder_output, decoder_hidden, decoder_attention = decoder(
decoder_input, decoder_hidden, encoder_outputs)
topv, topi = decoder_output.topk(1)
decoder_input = topi.squeeze().detach()
loss += criterion(decoder_output, target_tensor[di])
# 如果已经翻译完了
if decoder_input.item() == EOS_token:
break
# 反向传播
loss.backward()
# 梯度更新
encoder_optimizer.step()
decoder_optimizer.step()
return loss.item() / target_length
import time
import math
# 秒到分钟转换
def asMinutes(s):
m = math.floor(s / 60)
s -= m * 60
return '%dm %ds' % (m, s)
# 获取运行时间间隔
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
plot_loss_total = 0
# 随机梯度下降优化
encoder_optimiz.er = optim.SGD(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)
# 获取训练数据
training_pairs = [tensorsFromPair(random.choice(pairs))
for i in range(n_iters)]
# NLLLoss()+LogSoftmax()=CrossEntropy()
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
showPlot(plot_losses)
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import matplotlib.ticker as ticker
# 画图
def showPlot(points):
plt.figure()
fig, ax = plt.subplots()
loc = ticker.MultipleLocator(base=0.2)
ax.yaxis.set_major_locator(loc)
plt.plot(points)
# 模型验证
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) # 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)
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=10):
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('')
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)
trainIters(encoder1, attn_decoder1, 75000, print_every=5000)
evaluateRandomly(encoder1, attn_decoder1)
output_words, attentions = evaluate(
encoder1, attn_decoder1, "je suis trop froid .")
plt.matshow(attentions.numpy())
# 可视化注意力
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 .")