标签：长度 截断 datasets 其他 set list ken padding ras
利用 IMDB 数据进行 Sentiment Analysis。
通过 keras.datasets 里面下载，注意下载的结构，并进行预处理。
from keras.datasets import imdb from keras import preprocessing # Number of words to consider as features max_features = 10000 # Cut texts after this number of words # (among top max_features most common words) maxlen = 20 # Load the data as lists of integers. (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
x_train
y_train: 二分类 0 和 1
需要对文本长度进行调节
# This turns our lists of integers # into a 2D integer tensor of shape `(samples, maxlen)` x_train = preprocessing.sequence.pad_sequences(x_train, maxlen=maxlen) x_test = preprocessing.sequence.pad_sequences(x_test, maxlen=maxlen)
长度设置为 maxlen=20。
得到的矩阵可以直接作为 Embedding 层的输入数据。
keras.preprocessing.sequence.pad_sequences(sequences, maxlen=None, dtype=‘int32‘,
padding=‘pre‘, truncating=‘pre‘, value=0.)
将长为nb_samples的序列（标量序列）转化为形如(nb_samples,nb_timesteps)2D numpy array。如果提供了参数maxlen，nb_timesteps=maxlen，否则其值为最长序列的长度。其他短于该长度的序列都会在后部填充0以达到该长度。长于nb_timesteps的序列将会被截断，以使其匹配目标长度。padding和截断发生的位置分别取决于padding和truncating.
sequences：浮点数或整数构成的两层嵌套列表
maxlen：None或整数，为序列的最大长度。大于此长度的序列将被截短，小于此长度的序列将在后部填0.
dtype：返回的numpy array的数据类型
padding：‘pre’或‘post’，确定当需要补0时，在序列的起始还是结尾补
truncating：‘pre’或‘post’，确定当需要截断序列时，从起始还是结尾截断
value：浮点数，此值将在填充时代替默认的填充值0
返回形如(nb_samples,nb_timesteps)的2D张量
>>> a = np.array([[2, 3],
[3, 4, 6],
[7, 8, 9, 10]])
>>> a
array([list([2, 3]), list([3, 4, 6]), list([7, 8, 9, 10])], dtype=object)
>>> import keras
Using TensorFlow backend.
>>> b = keras.preprocessing.sequence.pad_sequences(a, maxlen=10)
>>> b
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 2, 3],
[ 0, 0, 0, 0, 0, 0, 0, 3, 4, 6],
[ 0, 0, 0, 0, 0, 0, 7, 8, 9, 10]])
>>> c = keras.preprocessing.sequence.pad_sequences(a, maxlen=10, padding=‘post‘)
>>> c
array([[ 2, 3, 0, 0, 0, 0, 0, 0, 0, 0],
[ 3, 4, 6, 0, 0, 0, 0, 0, 0, 0],
[ 7, 8, 9, 10, 0, 0, 0, 0, 0, 0]])
>>> d = keras.preprocessing.sequence.pad_sequences(a, maxlen=3, padding=‘post‘)
>>> d
array([[ 2, 3, 0],
[ 3, 4, 6],
[ 8, 9, 10]])
>>> e = keras.preprocessing.sequence.pad_sequences(a, maxlen=3)
>>> e
array([[ 0, 2, 3],
[ 3, 4, 6],
[ 8, 9, 10]])
>>> f = keras.preprocessing.sequence.pad_sequences(a, maxlen=3, padding=‘post‘, truncating=‘post‘)
>>> f
array([[2, 3, 0],
[3, 4, 6],
[7, 8, 9]])
标签：长度 截断 datasets 其他 set list ken padding ras
原文地址：https://www.cnblogs.com/alex-bn-lee/p/14196890.html
