Seq2seq+Attention 机器翻译

本文为🔗365天深度学习训练营内部文章

原作者：K同学啊

python 复制代码

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")

1.搭建语言类

python 复制代码

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

python 复制代码

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.文本读取函数

python 复制代码

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))

4.编码器

python 复制代码

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)

5.解码器加了Attention注意力机制

python 复制代码

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)

6.训练

python 复制代码

# 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)

# 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):
    encoder_hidden = encoder.initHidden()
    encoder_optimizer.zero_grad()
    decoder_optimizer.zero_grad()

    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

    # 编码阶段
    for ei in range(input_length):
        encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)
        encoder_outputs[ei] = encoder_output[0, 0]

    # 解码阶段
    decoder_input = torch.tensor([[SOS_token]], device=device)
    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(decoder_input, decoder_hidden, encoder_outputs)  # ✅ 传入 encoder_outputs
            loss += criterion(decoder_output, target_tensor[di])
            decoder_input = target_tensor[di]  # Teacher forcing
    else:
        for di in range(target_length):
            decoder_output, decoder_hidden, _ = decoder(decoder_input, decoder_hidden, encoder_outputs)  # ✅ 传入 encoder_outputs
            topv, topi = decoder_output.topk(1)
            decoder_input = topi.squeeze().detach()
            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

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=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('')

7.评估

python 复制代码

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)

python 复制代码

evaluateRandomly(encoder1,attn_decoder1)

8.Loss函数

python 复制代码

# 1.loss图
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()

9.可视化注意力

python 复制代码

# 2.可视化注意力
output_words,attentions = evaluate(encoder1,attn_decoder1,"je suis trop froid .")
plt.matshow(attentions.numpy())

python 复制代码

# 2.可视化注意力
output_words,attentions = evaluate(encoder1,attn_decoder1,"je suis trop froid .")
plt.matshow(attentions.numpy())

import matplotlib.ticker as ticker

def showAttentuon(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))
    showAttentuon(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 .')

Seq2seq+Attention 机器翻译

1.搭建语言类

2.文本处理函数

3.文本读取函数

4.编码器

5.解码器 加了Attention注意力机制

6.训练

7.评估

8.Loss函数

9.可视化注意力

5.解码器加了Attention注意力机制