机器翻译及其相关技术

机器翻译是指将⼀段⽂本从⼀种语⾔⾃动翻译到另⼀种语⾔。机器翻译用到了循环神经网络，但因为⼀段⽂本序列在不同语⾔中的⻓度不 ⼀定相同，比如输入是"I am chinese"机器翻译后输出是“我是中国人”，此时的输入是需要3个神经元而输出需要5个，长度是不同的，因此又不能用传统的循环神经网络。

导入包

import os
import sys
sys.path.append('/home/kesci/input/d2l9528/')
import collections
import d2l
import zipfile
from d2l.data.base import Vocab
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils import data
from torch import optim

数据集处理

进行数据清洗，将其转化为神经网络的输入minbatch

with open('/home/kesci/input/fraeng6506/fra.txt', 'r') as f:
      raw_text = f.read()
print(raw_text[0:1000])

此数据集是法语和英语的翻译，结果如下图所示，每一行是一个句子。

句子先是一个英语，标点符号，空格，法语，标点符号，还有其他的不需要的。

Go.	Va !	CC-BY 2.0 (France) Attribution: tatoeba.org #2877272 (CM) & #1158250 (Wittydev)
Hi.	Salut !	CC-BY 2.0 (France) Attribution: tatoeba.org #538123 (CM) & #509819 (Aiji)
Hi.	Salut.	CC-BY 2.0 (France) Attribution: tatoeba.org #538123 (CM) & #4320462 (gillux)
Run!	Cours !	CC-BY 2.0 (France) Attribution: tatoeba.org #906328 (papabear) & #906331 (sacredceltic)
Run!	Courez !	CC-BY 2.0 (France) Attribution: tatoeba.org #906328 (papabear) & #906332 (sacredceltic)
Who?	Qui ?	CC-BY 2.0 (France) Attribution: tatoeba.org #2083030 (CK) & #4366796 (gillux)
Wow!	Ça alors !	CC-BY 2.0 (France) Attribution: tatoeba.org #52027 (Zifre) & #374631 (zmoo)
Fire!	Au feu !	CC-BY 2.0 (France) Attribution: tatoeba.org #1829639 (Spamster) & #4627939 (sacredceltic)
Help!	À l'aide !	CC-BY 2.0 (France) Attribution: tatoeba.org #435084 (lukaszpp) & #128430 (sysko)
Jump.	Saute.	CC-BY 2.0 (France) Attribution: tatoeba.org #631038 (Shishir) & #2416938 (Phoenix)
Stop!	Ça suffit !	CC-BY 2.0 (France) Attribution: tato

首先要去掉乱码，大小写

def preprocess_raw(text):
    text = text.replace('\u202f', ' ').replace('\xa0', ' ')
    out = ''
    for i, char in enumerate(text.lower()):
        if char in (',', '!', '.') and i > 0 and text[i-1] != ' ':
            out += ' '
        out += char
    return out

text = preprocess_raw(raw_text)
print(text[0:1000])

结果如下：

和上面的结果相比，全部转换了小写字母且单词和标点符号之间加了空格。

go .	va !	cc-by 2 .0 (france) attribution: tatoeba .org #2877272 (cm) & #1158250 (wittydev)
hi .	salut !	cc-by 2 .0 (france) attribution: tatoeba .org #538123 (cm) & #509819 (aiji)
hi .	salut .	cc-by 2 .0 (france) attribution: tatoeba .org #538123 (cm) & #4320462 (gillux)
run !	cours !	cc-by 2 .0 (france) attribution: tatoeba .org #906328 (papabear) & #906331 (sacredceltic)
run !	courez !	cc-by 2 .0 (france) attribution: tatoeba .org #906328 (papabear) & #906332 (sacredceltic)
who?	qui ?	cc-by 2 .0 (france) attribution: tatoeba .org #2083030 (ck) & #4366796 (gillux)
wow !	ça alors !	cc-by 2 .0 (france) attribution: tatoeba .org #52027 (zifre) & #374631 (zmoo)
fire !	au feu !	cc-by 2 .0 (france) attribution: tatoeba .org #1829639 (spamster) & #4627939 (sacredceltic)
help !	à l'aide !	cc-by 2 .0 (france) attribution: tatoeba .org #435084 (lukaszpp) & #128430 (sysko)
jump .	saute .	cc-by 2 .0 (france) attribution: tatoeba .org #631038 (shishir) & #2416938 (phoenix)
stop !	ça suffit !	cc-b

字符在计算机里是以编码的形式存在，我们通常所用的空格是 \x20 ，是在标准ASCII可见字符 0x20~0x7e 范围内。 而 \xa0 属于 latin1 （ISO/IEC_8859-1）中的扩展字符集字符，代表不间断空白符nbsp(non-breaking space)，超出gbk编码范围，是需要去除的特殊字符。再数据预处理的过程中，我们首先需要对数据进行清洗。对数据进行清洗完成后需要进行分词。

分词

分词是将字符串分为单词组成的列表。

num_examples = 50000
source, target = [], []#sorce代表的英语的列表，target代表法语的列表
for i, line in enumerate(text.split('\n')):#每一行作为划分
    if i > num_examples:
        break
    parts = line.split('\t')
    if len(parts) >= 2:
        source.append(parts[0].split(' '))#空格把每个单词分开
        target.append(parts[1].split(' '))
        
source[0:3], target[0:3]

输出的结果为，我们已经得到了英语单词构成的一个列表，法语单词构成的一个列表

([['go', '.'], ['hi', '.'], ['hi', '.']],#英语的列表
 [['va', '!'], ['salut', '!'], ['salut', '.']])#法语的列表

建立词典

下面我们为英语和法语建立词典。即单词组成的列表---单词id组成的列表

第一步是取出所有单词，即把所有单词连接成一个单词列表（不去重，不做其他任何处理）。

def build_vocab(tokens):
    tokens = [token for line in tokens for token in line]
    return d2l.data.base.Vocab(tokens, min_freq=3, use_special_tokens=True)#Vocab类

src_vocab = build_vocab(source)#英语中单词列表的个数
len(src_vocab)

class Vocab(object):#Rhis class is saved in d2l
    def __init__(self, tokens, min_freq=0, use_special_tokens=False):
        #sort by frequency and token
        counter = collections.Counter(tokens)
        token_freqs = sorted(counter.items(), key = lambda x:x[0])
        token_freqs.sort(key = lambda x:x[1], reserved = True)
        if use_special_tokens:
            #padding, begin of sentence, end of sentence, unknown
            self.pad, self.bos, self.eos, self.unk = (0,1,2,3)
            tokens = ['', '', '', '']
        else:
            self.unk = 0
            tokens = ['']
            tokens += [token for token, freq in token_freqs if freq >= min_freq]
            self.idx_to_token = []
            self.token_to_idx = dict()
            for token in tokens:
                self.idx_to_token.append(token)
                self.token_to_idx[token]=len(self.idx_to_token)-1

def __len__(self):#单词表的长度（单词的个数）
    return len(self.idx_to_token)
        
def __getitem__(self, tokens):#输入一个单词列表，输出其对应的id
    if not isinstance(tokens, (list, tuple)):
        return self.token_to_idx.get(tokens, self.unk)
    else:
        return [self.__getitem__(token) for token in tokens] 

3789

载入数据集

def pad(line, max_len, padding_token):#保证输入的时候句子的长度是一样的，
    if len(line) > max_len:#如果一段话比maxlen长就将后面的删除
        return line[:max_len]
    return line + [padding_token] * (max_len - len(line))
pad(src_vocab[source[0]], 10, src_vocab.pad)#不足的进行补足

[38, 4, 0, 0, 0, 0, 0, 0, 0, 0]

def build_array(lines, vocab, max_len, is_source):
    lines = [vocab[line] for line in lines]
    if not is_source:#如果是target句子
        lines = [[vocab.bos] + line + [vocab.eos] for line in lines]#判断开始和结束
    array = torch.tensor([pad(line, max_len, vocab.pad) for line in lines])
    valid_len = (array != vocab.pad).sum(1) #第一个维度，保存其原本的长度不是padding后的长度
    return array, valid_len

 

def load_data_nmt(batch_size, max_len): # This function is saved in d2l.
    src_vocab, tgt_vocab = build_vocab(source), build_vocab(target)#生成词典
    src_array, src_valid_len = build_array(source, src_vocab, max_len, True)#英语
    tgt_array, tgt_valid_len = build_array(target, tgt_vocab, max_len, False)#法语
    train_data = data.TensorDataset(src_array, src_valid_len, tgt_array, tgt_valid_len)
    train_iter = data.DataLoader(train_data, batch_size, shuffle=True)
    return src_vocab, tgt_vocab, train_iter

 

src_vocab, tgt_vocab, train_iter = load_data_nmt(batch_size=2, max_len=8)#数据生成器
for X, X_valid_len, Y, Y_valid_len, in train_iter:
    print('X =', X.type(torch.int32), '\nValid lengths for X =', X_valid_len,
        '\nY =', Y.type(torch.int32), '\nValid lengths for Y =', Y_valid_len)
    break

X = tensor([[   5,   24,    3,    4,    0,    0,    0,    0],
        [  12, 1388,    7,    3,    4,    0,    0,    0]], dtype=torch.int32) 
Valid lengths for X = tensor([4, 5]) 
Y = tensor([[   1,   23,   46,    3,    3,    4,    2,    0],
        [   1,   15,  137,   27, 4736,    4,    2,    0]], dtype=torch.int32) 
Valid lengths for Y = tensor([7, 7])

Encoder-Decoder

encoder：输入到隐藏状态
decoder：隐藏状态到输出

class Encoder(nn.Module):
    def __init__(self, **kwargs):
        super(Encoder, self).__init__(**kwargs)

    def forward(self, X, *args):
        raise NotImplementedError

class Decoder(nn.Module):
    def __init__(self, **kwargs):
        super(Decoder, self).__init__(**kwargs)

    def init_state(self, enc_outputs, *args):
        raise NotImplementedError

    def forward(self, X, state):
        raise NotImplementedError

Sequence to Sequence模型

训练：

测试：

具体结构

class Seq2SeqEncoder(d2l.Encoder):
    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers,
                 dropout=0, **kwargs):
        super(Seq2SeqEncoder, self).__init__(**kwargs)
        self.num_hiddens=num_hiddens
        self.num_layers=num_layers
        self.embedding = nn.Embedding(vocab_size, embed_size)
        self.rnn = nn.LSTM(embed_size,num_hiddens, num_layers, dropout=dropout)
   
    def begin_state(self, batch_size, device):
        return [torch.zeros(size=(self.num_layers, batch_size, self.num_hiddens),  device=device),
                torch.zeros(size=(self.num_layers, batch_size, self.num_hiddens),  device=device)]
    def forward(self, X, *args):
        X = self.embedding(X) # X shape: (batch_size, seq_len, embed_size)
        X = X.transpose(0, 1)  # RNN needs first axes to be time
        # state = self.begin_state(X.shape[1], device=X.device)
        out, state = self.rnn(X)
        # The shape of out is (seq_len, batch_size, num_hiddens).
        # state contains the hidden state and the memory cell
        # of the last time step, the shape is (num_layers, batch_size, num_hiddens)
        return out, state

encoder = Seq2SeqEncoder(vocab_size=10, embed_size=8,num_hiddens=16, num_layers=2)
X = torch.zeros((4, 7),dtype=torch.long)
output, state = encoder(X)
output.shape, len(state), state[0].shape, state[1].shape

class Seq2SeqDecoder(d2l.Decoder):
    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers,
                 dropout=0, **kwargs):
        super(Seq2SeqDecoder, self).__init__(**kwargs)
        self.embedding = nn.Embedding(vocab_size, embed_size)
        self.rnn = nn.LSTM(embed_size,num_hiddens, num_layers, dropout=dropout)
        self.dense = nn.Linear(num_hiddens,vocab_size)

    def init_state(self, enc_outputs, *args):
        return enc_outputs[1]

    def forward(self, X, state):
        X = self.embedding(X).transpose(0, 1)
        out, state = self.rnn(X, state)
        # Make the batch to be the first dimension to simplify loss computation.
        out = self.dense(out).transpose(0, 1)
        return out, state

decoder = Seq2SeqDecoder(vocab_size=10, embed_size=8,num_hiddens=16, num_layers=2)
state = decoder.init_state(encoder(X))
out, state = decoder(X, state)
out.shape, len(state), state[0].shape, state[1].shape

损失函数

def SequenceMask(X, X_len,value=0):
    maxlen = X.size(1)
    mask = torch.arange(maxlen)[None, :].to(X_len.device) < X_len[:, None]   
    X[~mask]=value
    return X

X = torch.tensor([[1,2,3], [4,5,6]])
SequenceMask(X,torch.tensor([1,2]))

X = torch.ones((2,3, 4))
SequenceMask(X, torch.tensor([1,2]),value=-1)

class MaskedSoftmaxCELoss(nn.CrossEntropyLoss):
    # pred shape: (batch_size, seq_len, vocab_size)
    # label shape: (batch_size, seq_len)
    # valid_length shape: (batch_size, )
    def forward(self, pred, label, valid_length):
        # the sample weights shape should be (batch_size, seq_len)
        weights = torch.ones_like(label)
        weights = SequenceMask(weights, valid_length).float()
        self.reduction='none'
        output=super(MaskedSoftmaxCELoss, self).forward(pred.transpose(1,2), label)
        return (output*weights).mean(dim=1)

loss = MaskedSoftmaxCELoss()
loss(torch.ones((3, 4, 10)), torch.ones((3,4),dtype=torch.long), torch.tensor([4,3,0]))

训练：

def train_ch7(model, data_iter, lr, num_epochs, device):  # Saved in d2l
    model.to(device)
    optimizer = optim.Adam(model.parameters(), lr=lr)
    loss = MaskedSoftmaxCELoss()
    tic = time.time()
    for epoch in range(1, num_epochs+1):
        l_sum, num_tokens_sum = 0.0, 0.0
        for batch in data_iter:
            optimizer.zero_grad()
            X, X_vlen, Y, Y_vlen = [x.to(device) for x in batch]
            Y_input, Y_label, Y_vlen = Y[:,:-1], Y[:,1:], Y_vlen-1
            
            Y_hat, _ = model(X, Y_input, X_vlen, Y_vlen)
            l = loss(Y_hat, Y_label, Y_vlen).sum()
            l.backward()

            with torch.no_grad():
                d2l.grad_clipping_nn(model, 5, device)
            num_tokens = Y_vlen.sum().item()
            optimizer.step()
            l_sum += l.sum().item()
            num_tokens_sum += num_tokens
        if epoch % 50 == 0:
            print("epoch {0:4d},loss {1:.3f}, time {2:.1f} sec".format( 
                  epoch, (l_sum/num_tokens_sum), time.time()-tic))
            tic = time.time()

embed_size, num_hiddens, num_layers, dropout = 32, 32, 2, 0.0
batch_size, num_examples, max_len = 64, 1e3, 10
lr, num_epochs, ctx = 0.005, 300, d2l.try_gpu()
src_vocab, tgt_vocab, train_iter = d2l.load_data_nmt(batch_size, max_len,num_examples)
encoder = Seq2SeqEncoder(len(src_vocab), embed_size, num_hiddens, num_layers, dropout)
decoder = Seq2SeqDecoder(len(tgt_vocab), embed_size, num_hiddens, num_layers, dropout)
model = d2l.EncoderDecoder(encoder, decoder)
train_ch7(model, train_iter, lr, num_epochs, ctx)

测试：

def translate_ch7(model, src_sentence, src_vocab, tgt_vocab, max_len, device):
    src_tokens = src_vocab[src_sentence.lower().split(' ')]
    src_len = len(src_tokens)
    if src_len < max_len:
        src_tokens += [src_vocab.pad] * (max_len - src_len)
    enc_X = torch.tensor(src_tokens, device=device)
    enc_valid_length = torch.tensor([src_len], device=device)
    # use expand_dim to add the batch_size dimension.
    enc_outputs = model.encoder(enc_X.unsqueeze(dim=0), enc_valid_length)
    dec_state = model.decoder.init_state(enc_outputs, enc_valid_length)
    dec_X = torch.tensor([tgt_vocab.bos], device=device).unsqueeze(dim=0)
    predict_tokens = []
    for _ in range(max_len):
        Y, dec_state = model.decoder(dec_X, dec_state)
        # The token with highest score is used as the next time step input.
        dec_X = Y.argmax(dim=2)
        py = dec_X.squeeze(dim=0).int().item()
        if py == tgt_vocab.eos:
            break
        predict_tokens.append(py)
    return ' '.join(tgt_vocab.to_tokens(predict_tokens))

for sentence in ['Go .', 'Wow !', "I'm OK .", 'I won !']:
    print(sentence + ' => ' + translate_ch7(
        model, sentence, src_vocab, tgt_vocab, max_len, ctx))

参考：

https://www.kesci.com/org/boyuai/workspace/project