tflearn的VocabularyProcessor用法：建立
2019-10-10 本文已影响0人
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-- coding: utf-8 --
from hanziconv import HanziConv
from jieba import cut
from tflearn.data_utils import VocabularyProcessor
DOCUMENTS = [
'这是一条测试1',
'这是一条测试2',
'这是一条测试3',
'这是其他测试',
]
def chinese_tokenizer(documents):
"""
把中文文本转为词序列
"""
for document in documents:
# 繁体转简体
text = HanziConv.toSimplified(document)
# 英文转小写
text = text.lower()
# 分词
yield list(cut(text))
序列长度填充或截取到100，删除词频<=2的词
vocab = VocabularyProcessor(100, 2, tokenizer_fn=chinese_tokenizer)
创建词汇表，创建后不能更改
vocab.fit(DOCUMENTS)
保存和加载词汇表
vocab.save('vocab.pickle')
vocab = VocabularyProcessor.restore('vocab.pickle')
文本转为词ID序列，未知或填充用的词ID为0
id_documents = list(vocab.transform(DOCUMENTS))
for id_document in id_documents:
print(id_document)
[2 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[2 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[2 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
[2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
词ID序列转为文本
for document in vocab.reverse(id_documents):
print(document)
