基于神经网络的THUCNews数据集文本分类
时间: 2023-07-26 12:15:33 浏览: 48
您好,基于神经网络的THUCNews数据集文本分类可以使用深度学习框架如TensorFlow或PyTorch实现。以下是一个TensorFlow实现的示例代码:
```python
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Input, Embedding, Conv1D, GlobalMaxPooling1D, Dense
from tensorflow.keras.models import Model
# 加载数据集
train_data = open('thucnews_train.txt', 'r', encoding='utf-8').readlines()
test_data = open('thucnews_test.txt', 'r', encoding='utf-8').readlines()
# 预处理数据
tokenizer = Tokenizer(num_words=5000)
tokenizer.fit_on_texts(train_data)
x_train = tokenizer.texts_to_sequences(train_data)
x_test = tokenizer.texts_to_sequences(test_data)
x_train = pad_sequences(x_train, padding='post', maxlen=256)
x_test = pad_sequences(x_test, padding='post', maxlen=256)
num_classes = 14
# 构建模型
inputs = Input(shape=(256,))
x = Embedding(input_dim=5000, output_dim=128)(inputs)
x = Conv1D(filters=256, kernel_size=3, padding='valid', activation='relu')(x)
x = GlobalMaxPooling1D()(x)
outputs = Dense(num_classes, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)
# 编译模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# 训练模型
model.fit(x_train, y_train, epochs=10, batch_size=64, validation_data=(x_test, y_test))
```
在这个示例代码中,我们使用了一维卷积神经网络(Conv1D)对文本进行特征提取,然后通过全局最大池化(GlobalMaxPooling1D)来得到文本的特征表示。最后通过一个全连接层(Dense)输出分类结果。您可以根据需要调整模型的各种参数,如词汇量大小、卷积核大小、池化方式等。