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}.
import os imdb_dir = r"D:Deep LearningDataIMDBaclImdbaclImdb" 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)