July 2019
Intermediate to advanced
365 pages
8h 13m
Chinese
Content preview from Python机器学习手册：从数据预处理到深度学习
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20.17
文本分类
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339
20.17
文本分类
问题描述
对文本数据分类。
解决方案
使用长短期记忆递归神经网络 ：
#
加载库
import numpy as np
from keras.datasets import imdb
from keras.preprocessing import sequence
from keras import models
from keras import layers
#
设置随机种子
np.random.seed(0)
#
设置想要的特征数量
number_of_features = 1000
#
从影评数据中加载数据和目标向量
(data_train, target_train), (data_test, target_test) = imdb.load_data(
num_words=number_of_features)
#
采用添加填充值或者截断的方式，使每个样本都有
400
个特征
features_train = sequence.pad_sequences(data_train, maxlen=400)
features_test = sequence.pad_sequences(data_test, maxlen=400)
#
启动神经网络
network = models.Sequential()
#
添加嵌入层
network.add(layers.Embedding(input_dim=number_of_features, ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.