AI之机器翻译及相关技术

• 机器翻译和数据集
• 载入需要的包
• 数据预处理
• 分词
• 建立词典
• 载入数据集

Encoder-Decoder

Sequence to Sequence模型

• 模型：
• 训练
• 预测

具体结构：

• Encoder
• Decoder
• 损失函数
• 训练
• 测试

Beam Search

例题

机器翻译和数据集

机器翻译（MT）：将一段文本从一种语言自动翻译为另一种语言，用神经网络解决这个问题通常称为神经机器翻译（NMT）。
主要特征：输出是单词序列而不是单个单词。 输出序列的长度可能与源序列的长度不同。

载入需要的包

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

数据预处理

去除乱码，将数据集清洗、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])

输出结果：
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

def preprocess_raw(text):
text = text.replace('\u202f', ' ').replace('\xa0', ' ')
out = ''
for i, char in enumerate(text.lower()):#把大写改为小写，单词和标点符号之间加上空格
if char in (',', '!', '.') and i > 0 and text[i-1] != ' ':
out += ' '
out += char
return out

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

输出结果：
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。

分词

字符串—>单词组成的列表

num_examples = 50000
source, target = [], [] #英语、法语
for i, line in enumerate(text.split('\n')):
if i > num_examples:
break
parts = line.split('\t')
if len(parts) >= 2:
source.append(parts[0].split(' '))
target.append(parts[1].split(' '))

source[0:3], target[0:3]

输出结果：
([[‘go’, ‘.’], [‘hi’, ‘.’], [‘hi’, ‘.’]],
[[‘va’, ‘!’], [‘salut’, ‘!’], [‘salut’, ‘.’]])

d2l.set_figsize()
d2l.plt.hist([[len(l) for l in source], [len(l) for l in target]],label=['source', 'target'])
d2l.plt.legend(loc='upper right');

建立词典

单词组成的列表—单词id组成的列表

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)

src_vocab = build_vocab(source)
len(src_vocab)

输出结果：
3789
以下是调用的Vocab类函数详情：

class Vocab(object): # This 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], reverse=True)
if use_ special_ _tokens :
# padding, begin of sentence, end of sentence, unknown
self.pad, self.bos, self.eos, self.unk = (e, 1, 2, 3)
tokens = ['<pad>', '<bos>', '<eos>', '<unk>']
else:
self.unk = 0
tokens = [' <unk>']
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):
return len(self .idx_ to_ token)

def _ getitem  _(self, tokens):
if not isinstance(tokens, (list, tuple)):
return self .token_ to_ idx. get(tokens, self . unk)else:
return [self._ getitem__ (token) for token in tokens]

载入数据集

def pad(line, max_len, padding_token):
if len(line) > max_len:
return line[:max_len]
return line + [padding_token] * (max_len - len(line))
pad(src_vocab[source[0]], 10, src_vocab.pad)

输出结果：
[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:
lines = [[vocab.bos] + line + [vocab.eos] for line in lines]
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
return array, valid_len

class TensorDataset (Dataset) :
"""Dataset wrapping tensors .
Each sample will be retrieved by indexing tensors along the first dimension.
Arguments:
*tensors (Tensor): tensors that have the same size of the first dimension.
"""
def__ init__ (self, *tensors) :
assert all(tensors[0].size(0) == tensor.size(0) for tensor in tensors)self. tensors = tensors

def__ getitem__ (self, index):
return tuple (tensor[index] for tensor in self. tensors)

def _. len__ (self):
return self. tensors[0] .size(0)

def load_data_nmt(batch_size, max_len): # This function is saved in d2l.
src_vocab, tgt_vocab = build_vocab(source), build_vocab(target)
src_array, src_valid_len = build_array(source, src_vocab, max_len, True)
tgt_array, tgt_valid_len = build_array(target, tgt_vocab, max_len, False)
train_data = data.TensorDataset(src_array, src_valid_len, tgt_array, tgt_valid_len)#判断是否一一对应
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)
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

输出结果：
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
对话系统、翻译、生成式系统

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
class EncoderDecoder(nn.Module):
def __init__(self, encoder, decoder, **kwargs):
super(EncoderDecoder, self).__init__(**kwargs)
self.encoder = encoder
self.decoder = decoder

def forward(self, enc_X, dec_X, *args):
enc_outputs = self.encoder(enc_X, *args)
dec_state = self.decoder.init_state(enc_outputs, *args)
return self.decoder(dec_X, dec_state)

可以应用在对话系统、生成式任务中。

Sequence to Sequence模型

模型：

训练

预测

具体结构：

Encoder

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)#给每个单词赋上词向量
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 时序 故调换第一与第二维
# 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

输出结果：
(torch.Size([7, 4, 16]), 2, torch.Size([2, 4, 16]), torch.Size([2, 4, 16]))

Decoder

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

输出结果：
(torch.Size([4, 7, 10]), 2, torch.Size([2, 4, 16]), torch.Size([2, 4, 16]))

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]))
tensor([[1, 0, 0],
[4, 5, 0]])
X = torch.ones((2,3, 4))
SequenceMask(X, torch.tensor([1,2]),value=-1)

输出结果：
tensor([[[ 1., 1., 1., 1.],
[-1., -1., -1., -1.],
[-1., -1., -1., -1.]],
[[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[-1., -1., -1., -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]))

输出结果：
tensor([2.3026, 1.7269, 0.0000])

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)

输出结果：
epoch 50,loss 0.093, time 38.2 sec
epoch 100,loss 0.046, time 37.9 sec
epoch 150,loss 0.032, time 36.8 sec
epoch 200,loss 0.027, time 37.5 sec
epoch 250,loss 0.026, time 37.8 sec
epoch 300,loss 0.025, time 37.3 sec

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))

输出结果：
Go . => va !
Wow ! => !
I’m OK . => ça va .
I won ! => j’ai gagné !

Beam Search

简单greedy search：
缺点：只考虑每步当前最优解，前后语义是否连贯，是否能构成完整句子，不考虑全局。
若考虑全部组合，会造成搜索空间过大。

维特比算法：选择整体分数最高的句子（搜索空间太大）
集束搜索：可设置集束大小，举例取2。

例题

选择题
1.数据预处理中分词(Tokenization)的工作是？
1、把词语、标点用空格分开
2、把字符形式的句子转化为单词组成的列表
3、把句子转化为单词ID组成的列表
4、去除句子中的不间断空白符等特殊字符

2.不属于数据预处理工作的是？
1、得到数据生成器
2、建立词典
3、分词
4、把单词转化为词向量
答案解释
选项四：错误，单词转化为词向量是模型结构的一部分，词向量层一般作为网络的第一层。

3.下列不属于单词表里的特殊符号的是？
1、未知单词
2、空格符
3、句子开始符
4、句子结束符
答案解释
选项二：错误，参考建立词典部分代码，空格不被认为是特殊字符，在该项目里，空格在预处理时被去除。

4.关于集束搜索(Beam Search)说法错误的是
1、集束搜索结合了greedy search和维特比算法。
2、集束搜索使用beam size参数来限制在每一步保留下来的可能性词的数量。
3、集束搜索是一种贪心算法。
4、集束搜索得到的是全局最优解。

5.不属于Encoder-Decoder应用的是
1、机器翻译
2、对话机器人
3、文本分类任务
3、语音识别任务
答案解释
Encoder-Decoder常应用于输入序列和输出序列的长度是可变的，如选项一二四，而分类问题的输出是固定的类别，不需要使用Encoder-Decoder

6.关于Sequence to Sequence模型说法错误的是：
1、训练时decoder每个单元输出得到的单词作为下一个单元的输入单词。
2、预测时decoder每个单元输出得到的单词作为下一个单元的输入单词。
3、预测时decoder单元输出为句子结束符时跳出循环。
4、每个batch训练时encoder和decoder都有固定长度的输入。
答案解释
选项四：正确，每个batch的输入需要形状一致。

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