Tensorflow 词汇处理器

Question

我正在关注有关使用 tensorflow 进行文本分类的 wildml 博客。我无法理解代码语句中 max_document_length 的用途：

vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)

还有我如何从 vocab_processor

中提取词汇

Answer 1

我已经弄清楚如何从 vocabularyprocessor 对象中提取词汇。这对我来说非常有效。

import numpy as np
from tensorflow.contrib import learn

x_text = ['This is a cat','This must be boy', 'This is a a dog']
max_document_length = max([len(x.split(" ")) for x in x_text])

## Create the vocabularyprocessor object, setting the max lengh of the documents.
vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)

## Transform the documents using the vocabulary.
x = np.array(list(vocab_processor.fit_transform(x_text)))    

## Extract word:id mapping from the object.
vocab_dict = vocab_processor.vocabulary_._mapping

## Sort the vocabulary dictionary on the basis of values(id).
## Both statements perform same task.
#sorted_vocab = sorted(vocab_dict.items(), key=operator.itemgetter(1))
sorted_vocab = sorted(vocab_dict.items(), key = lambda x : x[1])

## Treat the id's as index into list and create a list of words in the ascending order of id's
## word with id i goes at index i of the list.
vocabulary = list(list(zip(*sorted_vocab))[0])

print(vocabulary)
print(x)

Answer 2

not able to understand the purpose of max_document_length

VocabularyProcessor 将您的文本文档映射为向量，并且您需要这些向量具有一致的长度。

您的输入数据记录的长度可能不同（或可能不会）。例如，如果您正在处理用于情感分析的句子，它们将具有不同的长度。

您将此参数提供给 VocabularyProcessor 以便它可以调整输出向量的长度。根据the documentation、

max_document_length: Maximum length of documents. if documents are longer, they will be trimmed, if shorter - padded.

查看 source code。

  def transform(self, raw_documents):
    """Transform documents to word-id matrix.
    Convert words to ids with vocabulary fitted with fit or the one
    provided in the constructor.
    Args:
      raw_documents: An iterable which yield either str or unicode.
    Yields:
      x: iterable, [n_samples, max_document_length]. Word-id matrix.
    """
    for tokens in self._tokenizer(raw_documents):
      word_ids = np.zeros(self.max_document_length, np.int64)
      for idx, token in enumerate(tokens):
        if idx >= self.max_document_length:
          break
        word_ids[idx] = self.vocabulary_.get(token)
      yield word_ids

注意行 word_ids = np.zeros(self.max_document_length)。

raw_documents 变量中的每一行都将映射到长度为 max_document_length 的向量。

Tensorflow 词汇处理器

Tensorflow vocabularyprocessor

vocabulary

tensorflow