如何从Keras的imdb数据集中恢复原始文本？

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我想从Keras的imdb数据集中恢复imdb的原始文本。首先，当我加载Keras的imdb数据集时，它返回单词索引序列：

>>> (X_train, y_train), (X_test, y_test) = imdb.load_data()>>> X_train[0]
[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, 22665, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 21631, 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, 19193, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 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, 10311, 8, 4, 107, 117, 5952, 15, 256, 4, 31050, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 12118, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 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, 5345, 19, 178, 32]

我找到了imdb.get_word_index method（），它返回单词索引字典，例如{'create'：984，'make'：94，…}。为了进行转换，我创建了索引词字典。

>>> word_index = imdb.get_word_index()>>> index_word = {v:k for k,v in word_index.items()}

然后，我尝试还原原始文本，如下所示。

>>> ' '.join(index_word.get(w) for w in X_train[5])"the effort still been that usually makes for of finished sucking ended cbc's an because before if just though something know novel female i i slowly lot of above freshened with connect in of script their that out end his deceptively i i"

所以为什么会这样呢？如何还原原始文字？

2021-02-24 10:25 更新

anna • 5050

共 1 个回答

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你的例子很不合适，它比一些缺失的停顿词要糟糕得多。

如果你重新阅读[keras.datasets.imdb.load_data]方法的start_char、oov_char和index_from 参数的文档，它们的解释如下：

start_char: int. 序列的开始将用这个字符标记。设置为1，因为0通常是填充字符。

oov_char: int.由于num_words或skip_top限制而被剪掉的单词将被这个字符替换。

index_from: int.使用此索引和更高的索引来索引实际单词。

你的字典假定单词索引从1开始。

但是我的keras返回<START>和<UNKNOWN>作为索引1和2。 (它假设你将使用0作为<PADDING>)。下面的语句对我有用：

import keras
NUM_WORDS=1000 # only use top 1000 words
INDEX_FROM=3   # word index offset

train,test = keras.datasets.imdb.load_data(num_words=NUM_WORDS, index_from=INDEX_FROM)
train_x,train_y = train
test_x,test_y = test

word_to_id = keras.datasets.imdb.get_word_index()
word_to_id = {k:(v+INDEX_FROM) for k,v in word_to_id.items()}
word_to_id["<PAD>"] = 0
word_to_id["<START>"] = 1
word_to_id["<UNK>"] = 2
word_to_id["<UNUSED>"] = 3

id_to_word = {value:key for key,value in word_to_id.items()}
print(' '.join(id_to_word[id] for id in train_x[0] ))

标点符号丢失了，仅此而已：

"<START> this film was just brilliant casting <UNK> <UNK> story
 direction <UNK> really <UNK> the part they played and you could just
 imagine being there robert <UNK> is an amazing actor ..."