keras.preprocessing.text.Tokenizer(num_words=None, filters='!"#$%&()*+,-./:;<=>?@[\]^_`{|}~ ', lower=True, split=' ', char_level=False, oov_token=None, document_count=0) 1 2 3 4 5 6 7 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...
fromkeras.preprocessing.textimporttext_to_word_sequence sentence ='Near is a good name, you should always be near to someone to save'seq = text_to_word_sequence(sentence)printseq# ['near', 'is', 'a', 'good', 'name', 'you', 'should', 'always', 'be', 'near', 'to', 'someone...
keras.preprocessing.text.Tokenizer(num_words=None,filters='!"#$%&()*+,-./:;<=>?@[\]^_`{|}~ ',lower=True,split=' ',char_level=False,oov_token=None,document_count=0)复制代码 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...
char_level: 如果为 True,则每个字符都将被视为标记。 oov_token: 如果给出,它将被添加到 word_index 中,并用于在 text_to_sequence 调用期间替换词汇表外的单词。 例如: from keras.preprocessing.textimportTokenizersomestr=['ha ha gua angry','howa ha gua excited naive'] tok = Tokenizer(num_words...
tensorflow.keras.preprocessing.text.Tokenizer中的文本编码与旧的tfds.deprecated.text.TokenTextEncoder有...
from keras.preprocessing.textimportTokenizer from keras.preprocessing.sequenceimportpad_sequencesif__name__=='__main__':dataset=pd.read_csv('sentiment_analysis/data_train.csv',sep='\t',names=['ID','type','review','label']).astype(str)cw=lambda x:list(jieba.cut(x))dataset['words']=da...
from sklearn.model_selection import train_test_split import pandas as pd import jieba from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences if __name__=='__main__': dataset = pd.read_csv('sentiment_analysis/data_train.csv', sep='\t',names=[...
作用:将文本向量化,或将文本转换为序列(即单个字词以及对应下标构成的列表,从1开始)的类。用来对文本进行分词预处理。 示例 import tensorflow as tf #Tokenizer 的示例 tokenizer = tf.keras.preprocessing.text.Tokenizer( filters='') text = ["昨天 天气 是 多云", "我 今天 做了 什么 呢"] ...
Text preprocessing:将文本特征映射到整数序列的预处理层。 Numerical features preprocessing layers:包括规范化层和离散化层 Numerical features preprocessing layers:包括类别编码层、哈希层、字符串查找层、整数查找层 Numerical features preprocessing layers:包括调整图像大小层、重新缩放层、中心裁剪层 Numerical features ...
>>> from keras.preprocessing.text import Tokenizer >>> tokenizer = Tokenizer(num_words=5000) >>> tokenizer.fit_on_texts(sentences_train) >>> X_train = tokenizer.texts_to_sequences(sentences_train) >>> X_test = tokenizer.texts_to_sequences(sentences_test) >>> vocab_size = len(tokenizer...