import os
import torch
from d2l import torch as d2l
下载和预处理数据集
d2l.DATA_HUB['fra-eng'] = (d2l.DATA_URL + 'fra-eng.zip',
'94646ad1522d915e7b0f9296181140edcf86a4f5')
def read_data_nmt():
"""载入“英语－法语”数据集。"""
data_dir = d2l.download_extract('fra-eng')
with open(os.path.join(data_dir, 'fra.txt'), 'r') as f:
return f.read()
raw_text = read_data_nmt()
print(raw_text[:75])
Go. Va ! Hi. Salut ! Run! Cours ! Run! Courez ! Who? Qui ? Wow! Ça alors !
几个预处理步骤
def preprocess_nmt(text):
"""预处理“英语－法语”数据集。"""
def no_space(char, prev_char):
return char in set(',.!?') and prev_char != ' '
text = text.replace('\u202f', ' ').replace('\xa0', ' ').lower()
out = [
' ' + char if i > 0 and no_space(char, text[i - 1]) else char
for i, char in enumerate(text)]
return ''.join(out)
text = preprocess_nmt(raw_text)
print(text[:80])
go . va ! hi . salut ! run ! cours ! run ! courez ! who ? qui ? wow ! ça alors !
词元化
def tokenize_nmt(text, num_examples=None):
"""词元化“英语－法语”数据数据集。"""
source, target = [], []
for i, line in enumerate(text.split('\n')):
if num_examples and i > num_examples:
break
parts = line.split('\t')
if len(parts) == 2:
source.append(parts[0].split(' '))
target.append(parts[1].split(' '))
return source, target
source, target = tokenize_nmt(text)
source[:6], target[:6]
([['go', '.'], ['hi', '.'], ['run', '!'], ['run', '!'], ['who', '?'], ['wow', '!']], [['va', '!'], ['salut', '!'], ['cours', '!'], ['courez', '!'], ['qui', '?'], ['ça', 'alors', '!']])
绘制每个文本序列所包含的标记数量的直方图
d2l.set_figsize()
_, _, patches = d2l.plt.hist([[len(l)
for l in source], [len(l) for l in target]],
label=['source', 'target'])
for patch in patches[1].patches:
patch.set_hatch('/')
d2l.plt.legend(loc='upper right');
词汇表
src_vocab = d2l.Vocab(source, min_freq=2,
reserved_tokens=['<pad>', '<bos>', '<eos>'])
len(src_vocab)
10012
序列样本都有一个固定的长度 截断或填充文本序列
def truncate_pad(line, num_steps, padding_token):
"""截断或填充文本序列。"""
if len(line) > num_steps:
return line[:num_steps]
return line + [padding_token] * (num_steps - len(line))
truncate_pad(src_vocab[source[0]], 10, src_vocab['<pad>'])
[47, 4, 1, 1, 1, 1, 1, 1, 1, 1]
转换成小批量数据集用于训练
def build_array_nmt(lines, vocab, num_steps):
"""将机器翻译的文本序列转换成小批量。"""
lines = [vocab[l] for l in lines]
lines = [l + [vocab['<eos>']] for l in lines]
array = torch.tensor([
truncate_pad(l, num_steps, vocab['<pad>']) for l in lines])
valid_len = (array != vocab['<pad>']).type(torch.int32).sum(1)
return array, valid_len
训练模型
def load_data_nmt(batch_size, num_steps, num_examples=600):
"""返回翻译数据集的迭代器和词汇表。"""
text = preprocess_nmt(read_data_nmt())
source, target = tokenize_nmt(text, num_examples)
src_vocab = d2l.Vocab(source, min_freq=2,
reserved_tokens=['<pad>', '<bos>', '<eos>'])
tgt_vocab = d2l.Vocab(target, min_freq=2,
reserved_tokens=['<pad>', '<bos>', '<eos>'])
src_array, src_valid_len = build_array_nmt(source, src_vocab, num_steps)
tgt_array, tgt_valid_len = build_array_nmt(target, tgt_vocab, num_steps)
data_arrays = (src_array, src_valid_len, tgt_array, tgt_valid_len)
data_iter = d2l.load_array(data_arrays, batch_size)
return data_iter, src_vocab, tgt_vocab
读出“英语－法语”数据集中的第一个小批量数据
train_iter, src_vocab, tgt_vocab = load_data_nmt(batch_size=2, num_steps=8)
for X, X_valid_len, Y, Y_valid_len in train_iter:
print('X:', X.type(torch.int32))
print('valid lengths for X:', X_valid_len)
print('Y:', Y.type(torch.int32))
print('valid lengths for Y:', Y_valid_len)
break
X: tensor([[ 24, 160, 4, 3, 1, 1, 1, 1],
[ 16, 60, 4, 3, 1, 1, 1, 1]], dtype=torch.int32)
valid lengths for X: tensor([4, 4])
Y: tensor([[13, 29, 0, 4, 3, 1, 1, 1],
[41, 53, 4, 3, 1, 1, 1, 1]], dtype=torch.int32)
valid lengths for Y: tensor([5, 4])