【458】keras 文本向量化 Vectorization

相关类与方法说明：

from keras.preprocessing.text import Tokenizer
Tokenizer：文本标记实用类。该类允许使用两种方法向量化一个文本语料库：将每个文本转化为一个整数序列（每个整数都是词典中标记的索引）；或者将其转化为一个向量，其中每个标记的系数可以是二进制值、词频、TF-IDF权重等。
- num_words: 需要保留的最大词数，基于词频。只有最常出现的 num_words 词会被保留。
tokenizer.fit_on_texts()：Updates internal vocabulary based on a list of texts.
tokenizer.texts_to_sequences()：Transforms each text in texts in a sequence of integers. Only top "num_words" most frequent words will be taken into account. Only words known by the tokenizer will be taken into account.
tokenizer.word_index：dict {word: index}.
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import os
imdb_dir = r"D:\Deep Learning\Data\IMDB\aclImdb\aclImdb"
train_dir = os.path.join(imdb_dir, 'train')
 
labels = []
texts = []
 
for label_type in ['neg', 'pos']:
    dir_name = os.path.join(train_dir, label_type)
    for fname in os.listdir(dir_name):
        if fname[-4:] == '.txt':
            f = open(os.path.join(dir_name, fname), encoding='UTF-8')
            texts.append(f.read())
            f.close()
            if label_type == 'neg':
                labels.append(0)
            else:
                labels.append(1)
 
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
import numpy as np
 
maxlen = 100
training_samples = 200
validation_samples = 10000
max_words = 10000
 
"""
Text tokenization utility class.
 
This class allows to vectorize a text corpus, by turning each
text into either a sequence of integers (each integer being the index
of a token in a dictionary) or into a vector where the coefficient
for each token could be binary, based on word count, based on tf-idf...
 
# Arguments
    num_words: the maximum number of words to keep, based
        on word frequency. Only the most common `num_words` words will
        be kept.
"""
tokenizer = Tokenizer(num_words=max_words)
# Updates internal vocabulary based on a list of texts.
tokenizer.fit_on_texts(texts)
# Transforms each text in texts in a sequence of integers.
# Only top "num_words" most frequent words will be taken into account.
# Only words known by the tokenizer will be taken into account.
sequences = tokenizer.texts_to_sequences(texts)
# dict {word: index}
word_index = tokenizer.word_index
 
print('Found %s unique tokens.' % len(word_index))
 
data = pad_sequences(sequences, maxlen=maxlen)
print('Shape of data tensor:', data.shape)