写一个使用GRU循环神经网络处理THUCNews数据集的代码

好的，下面是一个使用GRU循环神经网络处理THUCNews数据集的代码，供您参考：

import numpy as np
import pandas as pd
import jieba
import os
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from keras.layers import Input, Embedding, GRU, Dense
from keras.models import Model

# 读取数据集
train_df = pd.read_csv('THUCNews/train.csv', header=None, names=['label', 'text'])
test_df = pd.read_csv('THUCNews/test.csv', header=None, names=['label', 'text'])

# 中文分词
def cut_text(text):
    return ' '.join(jieba.cut(text))

train_df['text'] = train_df['text'].apply(cut_text)
test_df['text'] = test_df['text'].apply(cut_text)

# 创建词汇表
max_words = 10000
tokenizer = Tokenizer(num_words=max_words)
tokenizer.fit_on_texts(train_df['text'])
word_index = tokenizer.word_index

# 将文本转换为数字序列
maxlen = 200
train_sequences = tokenizer.texts_to_sequences(train_df['text'])
train_data = pad_sequences(train_sequences, maxlen=maxlen)

test_sequences = tokenizer.texts_to_sequences(test_df['text'])
test_data = pad_sequences(test_sequences, maxlen=maxlen)

# 标签处理
train_labels = to_categorical(train_df['label'] - 1)
test_labels = to_categorical(test_df['label'] - 1)

# 构建模型
embedding_dim = 100
input_layer = Input(shape=(maxlen,))
embedding_layer = Embedding(max_words, embedding_dim)(input_layer)
gru_layer = GRU(128)(embedding_layer)
output_layer = Dense(14, activation='softmax')(gru_layer)
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['acc'])

# 模型训练
model.fit(train_data, train_labels, epochs=5, batch_size=128, validation_data=(test_data, test_labels))

这个代码将THUCNews数据集进行了分词处理，并创建了一个包含10000个词汇的词汇表。然后将文本转换为数字序列，并将标签进行了处理。接着，使用GRU循环神经网络构建了一个模型，并对模型进行了训练。