python-keras文本分类：pretrain词向量+1D卷积神经网络

基于keras实现
利用之前训练好的词向量，基于keras使用1D卷积神经网络完成文本分类任务

准备工作
1：训练好的词向量
2：用于训练的文本（已完成分词，每篇文章且还有对应的label）

from __future__ import print_function
import os
import sys
import numpy as np
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from keras.layers import Dense, Input, Flatten
from keras.layers import Conv1D, MaxPooling1D, Embedding
from keras.models import Model
import gensim
import pandas as pd

MAX_SEQUENCE_LENGTH = 1000 # 每篇文章选取1000个词
MAX_NB_WORDS = 10000 # 将字典设置为含有1万个词
EMBEDDING_DIM = 300 # 词向量维度，300维
VALIDATION_SPLIT = 0.2 # 测试集大小，全部数据的20%

第一步：选取词频最高的一部分词
预训练好的词向量200万个词每个300维这个脚本的目的是实验性的将流程跑通模型训练过程没问题后再曾增加词的个数

# 目的是得到一份字典(embeddings_index)含有1万个词，每个词对应属于自己的300维向量
embeddings_index = {}

print('Indexing word vectors.')
path = '../word2vec_model'
model = gensim.models.Word2Vec.load(path)
word_vectors = model.wv
for word, vocab_obj in model.wv.vocab.items():
    if int(vocab_obj.index) < MAX_NB_WORDS:
        embeddings_index[word] = word_vectors[word]
del model, word_vectors # 删掉gensim模型释放内存
print('Found %s word vectors.' % len(embeddings_index))

# print out:
# Indexing word vectors.
# Found 10000 word vectors.

step 2 获取训练文本和对应的标签
我的训练数据保存成了csv文件，有三列 content, channel_id, name，其中的name与channel_id是一一对应的。content已经提前分好词。

print('Processing text dataset')

texts = []  # list of text samples
labels = []  # list of label ids
labels_index = {}  # label与name的对应关系

# 读取数据
path = '../content.csv'
contents = pd.read_csv(path)
contents = contents.dropna()

# 提取文本内容与label
texts = contents['content'].values.tolist()
labels = contents['channel_id'].map(int)
labels = labels.values.tolist()

# 获得label与name的对应关系
tem_labels_index = contents.groupby(['name', 'channel_id']).size().reset_index()
tem_labels_index = tem_labels_index[['channel_id', 'name']].values.tolist()
for idx, name in tem_labels_index:
    labels_index[name] = idx
del contents, tem_labels_index

print('Found %s texts.' % len(texts))

# print out
# Processing text dataset
# Found 57867 texts.

step 3
文本准备，keras相关函数在keras 文档 Text Preprocessing 部分可以找到

tokenizer = Tokenizer(num_words=MAX_NB_WORDS) # 传入我们词向量的字典
tokenizer.fit_on_texts(texts) # 传入我们的训练数据，得到训练数据中出现的词的字典
sequences = tokenizer.texts_to_sequences(texts) # 根据训练数据中出现的词的字典，将训练数据转换为sequences

word_index = tokenizer.word_index
print('Found %s unique tokens.' % len(word_index))

data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH) # 限制每篇文章的长度

labels = to_categorical(np.asarray(labels)) # label one hot表示
print('Shape of data tensor:', data.shape)
print('Shape of label tensor:', labels.shape)

# print out
# Found 379653 unique tokens.
# Shape of data tensor: (57867, 1000)
# Shape of label tensor: (57867, 26) # 我的文本类别有26类

step 4 准备训练集与测试集

# 打乱文章顺序
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
labels = labels[indices]
num_validation_samples = int(VALIDATION_SPLIT * data.shape[0])

# 切割数据
x_train = data[:-num_validation_samples]
y_train = labels[:-num_validation_samples]
x_val = data[-num_validation_samples:]
y_val = labels[-num_validation_samples:]

step 5 准备embedding layer

num_words = min(MAX_NB_WORDS, len(word_index))  # 对比词向量字典中包含词的个数与文本数据所有词的个数，取小
embedding_matrix = np.zeros((num_words, EMBEDDING_DIM))
for word, i in word_index.items():
    if i >= MAX_NB_WORDS:
        continue
    embedding_vector = embeddings_index.get(word)
    if embedding_vector is not None:
        # 文本数据中的词在词向量字典中没有，向量为取0；如果有则取词向量中该词的向量
        embedding_matrix[i] = embedding_vector

# 将预训练好的词向量加载如embedding layer
# 我们设置 trainable = False，代表词向量不作为参数进行更新
embedding_layer = Embedding(num_words,
                            EMBEDDING_DIM,
                            weights=[embedding_matrix],
                            input_length=MAX_SEQUENCE_LENGTH,
                            trainable=False)

step 6 训练模型

# 训练  1D 卷积神经网络 使用 Maxpooling1D
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
x = Conv1D(filters=128, kernel_size=5, activation='relu')(embedded_sequences)
x = MaxPooling1D((pool_size=5)(x)
x = Conv1D(filters=128, kernel_size=5, activation='relu')(x)
x = MaxPooling1D((pool_size=5)(x)
x = Conv1D(filters=128, kernel_size=5,, activation='relu')(x)
x = MaxPooling1D((pool_size=35)(x)
x = Flatten()(x)
x = Dense(128, activation='relu')(x)
preds = Dense(len(labels_index), activation='softmax')(x)

model = Model(sequence_input, preds)
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['acc'])

# 如果希望短一些时间可以，epochs调小
model.fit(x_train, y_train,
          batch_size=128,
          epochs=50,
          validation_data=(x_val, y_val))

python-keras文本分类：pretrain词向量+1D卷积神经网络

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