我试图通过添加一些单词的随机交换来增强imdb电影评论数据集。与图像数据不同，我不认为这个函数最初是在tensorflow中。例如，对于图像，您可以这样做
def transform(image, label):
image = tf.image.flip_left_right(image)
return image, label
使用tensorflow的原生函数来翻转图像。但对于扩增文本，我没看到tf。string中有这个功能。所以我使用的是textaugment中的Easy Data Augmentation实现。https://github.com/dsfsi/textaugment
,
try:
import textaugment
except ModuleNotFoundError:
!pip install textaugment
import textaugment
from textaugment import EDA
import nltk
nltk.download('stopwords')
t = EDA()
t.random_swap("John is going to town")
返回"约翰进城">
但是现在当我尝试使用这个random_swap命令来增加整个imdb评论数据集时，它遇到了一个错误，因为它试图作用于张量。
的例子:
try:
import textaugment
except ModuleNotFoundError:
!pip install textaugment
import textaugment
import pandas as pd
import tensorflow as tf
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras.models import Sequential
from tensorflow.keras.datasets import imdb
# set parameters:
max_features = 5000
maxlen = 400
batch_size = 32
embedding_dims = 50
filters = 250
kernel_size = 3
hidden_dims = 250
epochs = 1
runs = 1
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')
print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
from textaugment import EDA
import nltk
nltk.download('stopwords')
t = EDA()
for text in x_train:
text = t.random_swap(text)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-7fc9edb2f37b> in <module>()
1 for text in x_train:
----> 2 text = t.random_swap(text)
1 frames
/usr/local/lib/python3.7/dist-packages/textaugment/eda.py in validate(**kwargs)
72 raise TypeError("p must be a fraction between 0 and 1")
73 if 'sentence' in kwargs:
---> 74 if not isinstance(kwargs['sentence'].strip(), str) or len(kwargs['sentence'].strip()) == 0:
75 raise TypeError("sentence must be a valid sentence")
76 if 'n' in kwargs:
AttributeError: 'numpy.ndarray' object has no attribute 'strip'
那么如何在TensorFlow中增强数据，当本地命令不存在，你想做一个自定义函数来做增强?
通过使用imdb.load_data()加载数据集，您不会获得作为文本的电影评论。它已经被预处理过:评论(单词序列)已经被转换成整数序列，其中每个整数代表字典中的一个特定单词。
由于这个原因，您不能对它应用t.random_swap(text)。你得先把这些评论翻译成英文。
因此你需要相应的word_index。它是一个字典映射字到整数索引
在下一步中，您应该反转它，以获得将整数索引映射到单词的字典。请注意，索引被偏移3，因为0、1和2是保留索引，用于填充序列的开始，并且未知。你可以在这里找到更多细节。
word_index = imdb.get_word_index()
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
你应该在之前解码评论你将sequence.pad_sequences()应用于它们。否则评论中会出现很多用0表示的未知单词。
对于print(x_train[0])，您将得到:
[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 2, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 2, 19, 178, 32]
让我们解码这篇评论:
decoded_review = ' '.join([reverse_word_index.get(i - 3, '?') for i in x_train[0]])
你会得到:
print(decoded_review)
>>> "? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly ? was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little ? that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big ? for the whole film but these children are amazing and should be ? for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was ? with us all"
在评论被解码回文本后，您可以使用t.random_swap(decoded_review)来扩展它们。扩充后的数据可以用word_index字典编码回整数序列。