如何用词向量做文本分类（embedding+cnn）
1、数据简介
本文使用的数据集是著名的”20 Newsgroup dataset”。该数据集共有20种新闻文本数据，我们将实现对该数据集的文本分类任务。数据集的说明和下载请参考（http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20.html）。本文使用GloVe词向量。GloVe 是 “Global Vectors for Word Representation”的缩写，一种基于共现矩阵分解的词向量。本文所使用的GloVe词向量是在2014年的英文维基百科上训练的，有400k个不同的词，每个词用100维向量表示。链接（http://nlp.stanford.edu/data/glove.6B.zip） (友情提示，词向量文件大小约为822M)
2、数据预处理
我们首先遍历下语料文件下的所有文件夹，获得不同类别的新闻以及对应的类别标签，代码如下所示
texts = [] # list of text samples
labels_index = {} # dictionary mapping label name to numeric id
labels = [] # list of label ids
import os
TEXT_DATA_DIR = 'e:/textm/20_newsgroup'
for name in sorted(os.listdir(TEXT_DATA_DIR)):
path = os.path.join(TEXT_DATA_DIR, name)
if os.path.isdir(path):
label_id = len(labels_index)
labels_index[name] = label_id
if label_id == 2:
break
for fname in sorted(os.listdir(path)):
if fname.isdigit():
fpath = os.path.join(path, fname)
f = open(fpath,'r',encoding='latin-1')
texts.append(f.read().strip())
f.close()
labels.append(label_id)
print('Found %s texts.' % len(texts))
print(texts[0])
print(labels)
之后，我们可以新闻样本转化为神经网络训练所用的张量。所用到的Keras库是keras.preprocessing.text.Tokenizer和keras.preprocessing.sequence.pad_sequences。代码如下所示
######，我们可以新闻样本转化为神经网络训练所用的张量。
# 所用到的Keras库是keras.preprocessing.text.Tokenizer和keras.preprocessing.sequence.pad_sequences。代码如下所示
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
import numpy as np
tokenizer = Tokenizer()
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
word_index = tokenizer.word_index
print('Found %s unique tokens.' % len(word_index))
data = pad_sequences(sequences)
# from keras.utils import np_utils
# labels = np_utils.to_categorical(np.asarray(labels))
print('Shape of data tensor:', data.shape)
# split the data into a training set and a validation set
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
labels_new = []
for i in indices:
labels_new.append(labels[i])
nb_validation_samples = int(0.8 * data.shape[0])
x_train = data[:-nb_validation_samples]
y_train = labels_new[:-nb_validation_samples]
x_val = data[-nb_validation_samples:]
y_val = labels_new[-nb_validation_samples:]
print(x_train[0])接下来，我们从GloVe文件中解析出每个词和它所对应的词向量，并用字典的方式存储
###############读取词向量
embeddings_index = {}
f = open(os.path.join('E:\\textm', 'glove.6B.100d.txt'),'r',encoding='utf-8')
for line in f.readlines():
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
f.close()
print('Found %s word vectors.' % len(embeddings_index))此时，我们可以根据得到的字典生成上文所定义的词向量矩阵
#############我们可以根据得到的字典生成上文所定义的词向量矩阵
embedding_matrix = np.zeros((len(word_index) + 1, 100))
for word, i in word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
print(embedding_matrix)
#########我们将这个词向量矩阵加载到Embedding层中，注意，我们设置trainable=False使得这个编码层不可再训练。
from keras.layers import Embedding
embedding_layer = Embedding(len(word_index) + 1,
100,
weights=[embedding_matrix],
input_length=10036,
trainable=False)
3、训练模型
用到了三层卷积
from keras.models import *
from keras.layers import *
from keras.applications import *
from keras.preprocessing.image import *
sequence_input = Input(shape=(10036,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
x = Conv1D(128, 5, activation='relu')(embedded_sequences)
x = MaxPooling1D(5)(x)
x = Conv1D(128, 5, activation='relu')(x)
x = MaxPooling1D(5)(x)
x = Conv1D(128, 5, activation='relu')(x)
x = MaxPooling1D(35)(x) # global max pooling
x = Flatten()(x)
x = Dense(128, activation='relu')(x)
preds = Dense(1, activation='sigmoid')(x)
model = Model(sequence_input, preds)
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['acc'])
# happy learning!
model.fit(x_train, y_train, validation_data=(x_val, y_val),
nb_epoch=4, batch_size=128)
model.save('e:/mymodel.h5')全部代码如下
texts = [] # list of text samples
labels_index = {} # dictionary mapping label name to numeric id
labels = [] # list of label ids
import os
TEXT_DATA_DIR = 'e:/textm/20_newsgroup'
for name in sorted(os.listdir(TEXT_DATA_DIR)):
path = os.path.join(TEXT_DATA_DIR, name)
if os.path.isdir(path):
label_id = len(labels_index)
labels_index[name] = label_id
if label_id == 2:
break
for fname in sorted(os.listdir(path)):
if fname.isdigit():
fpath = os.path.join(path, fname)
f = open(fpath,'r',encoding='latin-1')
texts.append(f.read().strip())
f.close()
labels.append(label_id)
print('Found %s texts.' % len(texts))
print(texts[0])
print(labels)
######，我们可以新闻样本转化为神经网络训练所用的张量。
# 所用到的Keras库是keras.preprocessing.text.Tokenizer和keras.preprocessing.sequence.pad_sequences。代码如下所示
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
import numpy as np
tokenizer = Tokenizer()
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
word_index = tokenizer.word_index
print('Found %s unique tokens.' % len(word_index))
data = pad_sequences(sequences)
# from keras.utils import np_utils
# labels = np_utils.to_categorical(np.asarray(labels))
print('Shape of data tensor:', data.shape)
# split the data into a training set and a validation set
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
labels_new = []
for i in indices:
labels_new.append(labels[i])
nb_validation_samples = int(0.8 * data.shape[0])
x_train = data[:-nb_validation_samples]
y_train = labels_new[:-nb_validation_samples]
x_val = data[-nb_validation_samples:]
y_val = labels_new[-nb_validation_samples:]
print(x_train[0])
###############读取词向量
embeddings_index = {}
f = open(os.path.join('E:\\textm', 'glove.6B.100d.txt'),'r',encoding='utf-8')
for line in f.readlines():
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
f.close()
print('Found %s word vectors.' % len(embeddings_index))
#############我们可以根据得到的字典生成上文所定义的词向量矩阵
embedding_matrix = np.zeros((len(word_index) + 1, 100))
for word, i in word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
print(embedding_matrix)
#########我们将这个词向量矩阵加载到Embedding层中，注意，我们设置trainable=False使得这个编码层不可再训练。
from keras.layers import Embedding
embedding_layer = Embedding(len(word_index) + 1,
100,
weights=[embedding_matrix],
input_length=10036,
trainable=False)
from keras.models import *
from keras.layers import *
from keras.applications import *
from keras.preprocessing.image import *
sequence_input = Input(shape=(10036,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
x = Conv1D(128, 5, activation='relu')(embedded_sequences)
x = MaxPooling1D(5)(x)
x = Conv1D(128, 5, activation='relu')(x)
x = MaxPooling1D(5)(x)
x = Conv1D(128, 5, activation='relu')(x)
x = MaxPooling1D(35)(x) # global max pooling
x = Flatten()(x)
x = Dense(128, activation='relu')(x)
preds = Dense(1, activation='sigmoid')(x)
model = Model(sequence_input, preds)
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['acc'])
# happy learning!
model.fit(x_train, y_train, validation_data=(x_val, y_val),
nb_epoch=4, batch_size=128)
model.save('e:/mymodel.h5')4、参考文献
http://keras-cn.readthedocs.io/en/latest/blog/word_embedding/