Python代码实现基于神经网络的THUCNews数据集文本分类

好的，以下是基于神经网络的THUCNews数据集文本分类的Python代码实现：

import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
from sklearn.model_selection import train_test_split

# 加载数据集
def load_data(path, num_words, max_len, test_size=0.2):
    with open(path, 'r', encoding='utf-8') as f:
        lines = f.readlines()
    texts, labels = [], []
    for line in lines:
        label, text = line.strip().split('\t')
        texts.append(text)
        labels.append(label)
    tokenizer = keras.preprocessing.text.Tokenizer(num_words=num_words)
    tokenizer.fit_on_texts(texts)
    sequences = tokenizer.texts_to_sequences(texts)
    word_index = tokenizer.word_index
    data = pad_sequences(sequences, maxlen=max_len)
    labels = to_categorical(np.asarray(labels, dtype='int32'))
    x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=test_size)
    return x_train, y_train, x_test, y_test, word_index

# 定义模型
def define_model(max_len, word_index, num_classes):
    model = keras.Sequential()
    model.add(keras.layers.Embedding(len(word_index) + 1, 128, input_length=max_len))
    model.add(keras.layers.Conv1D(64, 5, activation='relu'))
    model.add(keras.layers.MaxPooling1D(5))
    model.add(keras.layers.Conv1D(64, 5, activation='relu'))
    model.add(keras.layers.MaxPooling1D(5))
    model.add(keras.layers.Flatten())
    model.add(keras.layers.Dense(128, activation='relu'))
    model.add(keras.layers.Dense(num_classes, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

# 训练模型
def train_model(model, x_train, y_train, x_test, y_test, batch_size, epochs):
    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test))

# 评估模型
def evaluate_model(model, x_test, y_test):
    loss, accuracy = model.evaluate(x_test, y_test)
    print('loss: {}\naccuracy: {}'.format(loss, accuracy))

# 预测新数据
def predict_new_data(model, text, word_index, max_len):
    tokenizer = keras.preprocessing.text.Tokenizer(num_words=len(word_index))
    tokenizer.fit_on_texts(text)
    sequences = tokenizer.texts_to_sequences(text)
    data = pad_sequences(sequences, maxlen=max_len)
    result = model.predict(data)
    return result

# 主函数
if __name__ == "__main__":
    path = 'THUCNews_train.txt'
    num_words = 5000
    max_len = 100
    batch_size = 64
    epochs = 10
    num_classes = 10
    x_train, y_train, x_test, y_test, word_index = load_data(path, num_words, max_len)
    model = define_model(max_len, word_index, num_classes)
    train_model(model, x_train, y_train, x_test, y_test, batch_size, epochs)
    evaluate_model(model, x_test, y_test)

注：以上代码中的 THUCNews_train.txt 是 THUCNews 数据集的训练集，需要自行下载。此外，该代码只实现了简单的卷积神经网络模型，仅供参考，具体的模型结构和参数需要根据实际情况进行调整。