自然语言处理 基于神经网络的THUCNews 代码和得到的结果

THUCNews是一个中文文本分类数据集，可以使用基于神经网络的方法来进行文本分类。以下是一个使用Python和PyTorch框架实现THUCNews文本分类的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
from torch.autograd import Variable
import numpy as np
import pandas as pd
import jieba
import re

# 读取数据集并进行预处理
def read_data(file):
    data = pd.read_csv(file, header=None, sep='\t')
    data.columns = ['label', 'content']
    data = data.dropna()
    data = data.reset_index(drop=True)
    data['content'] = data['content'].apply(lambda x: re.sub('\s+', ' ', x))
    data['content'] = data['content'].apply(lambda x: ' '.join(jieba.cut(x)))
    return data

# 构建词典
def build_vocab(data):
    word_dict = {}
    for content in data['content']:
        for word in content.split(' '):
            if word not in word_dict:
                word_dict[word] = len(word_dict) + 1
    return word_dict

# 将文本转化为词向量
def text2vec(data, word_dict):
    content_vec = []
    for content in data['content']:
        content_vec.append([word_dict[word] for word in content.split(' ')])
    return content_vec

# 定义模型
class TextCNN(nn.Module):
    def __init__(self, vocab_size, embedding_dim, class_num, kernel_num, kernel_sizes, dropout):
        super(TextCNN, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.convs = nn.ModuleList([nn.Conv2d(1, kernel_num, (K, embedding_dim)) for K in kernel_sizes])
        self.dropout = nn.Dropout(dropout)
        self.fc = nn.Linear(len(kernel_sizes) * kernel_num, class_num)

    def forward(self, x):
        x = self.embedding(x)
        x = x.unsqueeze(1)
        x = [nn.functional.relu(conv(x)).squeeze(3) for conv in self.convs]
        x = [nn.functional.max_pool1d(i, i.size(2)).squeeze(2) for i in x]
        x = torch.cat(x, 1)
        x = self.dropout(x)
        logit = self.fc(x)
        return logit

# 定义训练函数
def train(model, train_iter, val_iter, optimizer, criterion, num_epochs):
    for epoch in range(num_epochs):
        running_loss = 0.0
        running_acc = 0.0
        model.train()
        for batch in train_iter:
            x, y = batch.content, batch.label
            x = Variable(x)
            y = Variable(y)
            optimizer.zero_grad()
            output = model(x)
            loss = criterion(output, y)
            running_loss += loss.data[0] * y.size(0)
            _, pred = torch.max(output, 1)
            num_correct = (pred == y).sum()
            running_acc += num_correct.data[0]
            loss.backward()
            optimizer.step()
        epoch_loss = running_loss / len(train_data)
        epoch_acc = running_acc / len(train_data)
        val_loss, val_acc = evaluate(model, val_iter, criterion)
        print('Epoch: {}, Training Loss: {:.4f}, Training Acc: {:.4f}, Validation Loss: {:.4f}, Validation Acc: {:.4f}'.format(epoch+1, epoch_loss, epoch_acc, val_loss, val_acc))

# 定义评估函数
def evaluate(model, val_iter, criterion):
    running_loss = 0.0
    running_acc = 0.0
    model.eval()
    for batch in val_iter:
        x, y = batch.content, batch.label
        x = Variable(x)
        y = Variable(y)
        output = model(x)
        loss = criterion(output, y)
        running_loss += loss.data[0] * y.size(0)
        _, pred = torch.max(output, 1)
        num_correct = (pred == y).sum()
        running_acc += num_correct.data[0]
    epoch_loss = running_loss / len(val_data)
    epoch_acc = running_acc / len(val_data)
    return epoch_loss, epoch_acc

# 设置超参数
vocab_size = 50000
embedding_dim = 100
class_num = 14
kernel_num = 100
kernel_sizes = [3, 4, 5]
dropout = 0.5
lr = 1e-3
batch_size = 128
num_epochs = 10

# 读取数据集
train_data = read_data('train.txt')
val_data = read_data('val.txt')
test_data = read_data('test.txt')

# 构建词典
word_dict = build_vocab(train_data)

# 将文本转化为词向量
train_content_vec = text2vec(train_data, word_dict)
val_content_vec = text2vec(val_data, word_dict)
test_content_vec = text2vec(test_data, word_dict)

# 将词向量转化为Tensor
train_content_tensor = torch.LongTensor(train_content_vec)
train_label_tensor = torch.LongTensor(np.array(train_data['label']))
val_content_tensor = torch.LongTensor(val_content_vec)
val_label_tensor = torch.LongTensor(np.array(val_data['label']))
test_content_tensor = torch.LongTensor(test_content_vec)
test_label_tensor = torch.LongTensor(np.array(test_data['label']))

# 构建数据集和数据迭代器
train_dataset = data.TensorDataset(train_content_tensor, train_label_tensor)
val_dataset = data.TensorDataset(val_content_tensor, val_label_tensor)
test_dataset = data.TensorDataset(test_content_tensor, test_label_tensor)
train_iter = data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_iter = data.DataLoader(val_dataset, batch_size=batch_size, shuffle=True)
test_iter = data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True)

# 初始化模型、优化器和损失函数
model = TextCNN(vocab_size, embedding_dim, class_num, kernel_num, kernel_sizes, dropout)
optimizer = optim.Adam(model.parameters(), lr=lr)
criterion = nn.CrossEntropyLoss()

# 训练模型并评估
train(model, train_iter, val_iter, optimizer, criterion, num_epochs)
test_loss, test_acc = evaluate(model, test_iter, criterion)
print('Test Loss: {:.4f}, Test Acc: {:.4f}'.format(test_loss, test_acc))

以上代码中使用了TextCNN模型，它是一种基于卷积神经网络的文本分类模型。训练过程中使用了Adam优化器和交叉熵损失函数。在训练完模型后，我们使用测试集对模型进行了评估，并输出了测试集上的损失和准确率。